import warnings
warnings.simplefilter(action='ignore')
import pandas as pd
import numpy as np
from math import *
%matplotlib notebook
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.figure_factory as ff
import scipy.stats as sp
from scipy import interpolate
from scipy.spatial import cKDTree
from datetime import datetime
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
pd.set_option('display.max_columns', 1000)
pd.set_option("display.max_rows", 3000)
Using 2018, 2019 accidents report data for the maximum relevance to current time frame, in terms of accident event details.
acc = pd.read_csv('./data/dftRoadSafetyData_Accidents_2018.csv')
ac1 = pd.read_csv('./data/Road Safety Data - Accidents 2019.csv')
acc = pd.concat([acc, ac1])
print(acc.shape)
display(acc.head())
(240171, 32)
| Accident_Index | Location_Easting_OSGR | Location_Northing_OSGR | Longitude | Latitude | Police_Force | Accident_Severity | Number_of_Vehicles | Number_of_Casualties | Date | Day_of_Week | Time | Local_Authority_(District) | Local_Authority_(Highway) | 1st_Road_Class | 1st_Road_Number | Road_Type | Speed_limit | Junction_Detail | Junction_Control | 2nd_Road_Class | 2nd_Road_Number | Pedestrian_Crossing-Human_Control | Pedestrian_Crossing-Physical_Facilities | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Special_Conditions_at_Site | Carriageway_Hazards | Urban_or_Rural_Area | Did_Police_Officer_Attend_Scene_of_Accident | LSOA_of_Accident_Location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | 529150.0 | 182270.0 | -0.139737 | 51.524587 | 1 | 3 | 2 | 2 | 01/01/2018 | 2 | 01:30 | 2 | E09000007 | 3 | 501 | 3 | 30 | 0 | -1 | -1 | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 1 | 1 | E01000854 |
| 1 | 2018010080973 | 542020.0 | 184290.0 | 0.046471 | 51.539651 | 1 | 3 | 1 | 1 | 01/01/2018 | 2 | 00:50 | 17 | E09000025 | 4 | 165 | 6 | 30 | 2 | 4 | 6 | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 1 | 1 | E01003531 |
| 2 | 2018010080974 | 531720.0 | 182910.0 | -0.102474 | 51.529746 | 1 | 3 | 2 | 1 | 01/01/2018 | 2 | 00:45 | 3 | E09000019 | 3 | 1 | 6 | 20 | 6 | 4 | 5 | 0 | 0 | 5 | 4 | 1 | 1 | 0 | 0 | 1 | 1 | E01002723 |
| 3 | 2018010080981 | 541450.0 | 183220.0 | 0.037828 | 51.530179 | 1 | 2 | 2 | 1 | 01/01/2018 | 2 | 03:00 | 17 | E09000025 | 4 | 167 | 3 | 30 | 7 | 2 | 3 | 124 | 0 | 5 | 4 | 2 | 2 | 0 | 0 | 1 | 1 | E01003492 |
| 4 | 2018010080982 | 543580.0 | 176500.0 | 0.065781 | 51.469258 | 1 | 2 | 2 | 2 | 01/01/2018 | 2 | 02:20 | 6 | E09000011 | 3 | 207 | 6 | 30 | 0 | -1 | -1 | 0 | 0 | 0 | 4 | 1 | 2 | 0 | 0 | 1 | 1 | E01001682 |
The latitude and longitude give accurate location of each accident event and the LSOA- Lower Layer Super Output Area are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales. LSOAs have an average population of 1500 people or 650 households and consist of constituent Output Areas. Output Areas are a finer division of geographical area and consist of 310 households on an average.
Thus, keeping only LSOA of accident location and (Lat, Long) features for geo-location details of accident events and dropping - (Local_Authority_(District), Local_Authority_(Highway), 1st_Road_Number, 2nd_Road_Number)
Police Force gives information about the region wise police force that was responsible for reporting and investigating the event. Did_Police_Officer_Attend_Scene_of_Accident is a binary valued variable that gives post-event information, which is inconsequential. Thus, dropping these.
Number_of_Vehicles and Number_of_Casualties are features that give information about the specific details of the accident and not about anything that may help establish causality or correlation between the environment and the chances of an accident to occur.
Date, Day_of_Week and Time help find patterns and correlations between specific triggers for accidents such as festive seasons, peak traffic timings at rush hour, day and time of the week that are prone to more accidents due to certain causal factors.
1st_Road_Class, Road_Type, Junction_Detail, Junction_Control, 2nd_Road_Class, Road_Surface_Conditions, Special_Conditions_at_Site, Urban_or_Rural_Area give information about the finer details of the location of accidents that may act in conjunction to help identify patterns and alert the algorithm about potential risks of an accident. For eg: The predictive algorithm may identify that A(M) motorway roads with a roundabout or a staggered junction with damp road surface conditions are more prone to severe accidents than their counterparts.
Similarly, Light_Conditions and Weather_Conditions also provide information about climatic conditions that make certain scenarios more prone to accidents than others.
acc.columns
Index(['Accident_Index', 'Location_Easting_OSGR', 'Location_Northing_OSGR',
'Longitude', 'Latitude', 'Police_Force', 'Accident_Severity',
'Number_of_Vehicles', 'Number_of_Casualties', 'Date', 'Day_of_Week',
'Time', 'Local_Authority_(District)', 'Local_Authority_(Highway)',
'1st_Road_Class', '1st_Road_Number', 'Road_Type', 'Speed_limit',
'Junction_Detail', 'Junction_Control', '2nd_Road_Class',
'2nd_Road_Number', 'Pedestrian_Crossing-Human_Control',
'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions',
'Special_Conditions_at_Site', 'Carriageway_Hazards',
'Urban_or_Rural_Area', 'Did_Police_Officer_Attend_Scene_of_Accident',
'LSOA_of_Accident_Location'],
dtype='object')
acc = acc[[
'Accident_Index', 'LSOA_of_Accident_Location', 'Longitude', 'Latitude',
'Date', 'Day_of_Week', 'Time', '1st_Road_Class', 'Road_Type',
'Speed_limit', 'Junction_Detail', 'Junction_Control', '2nd_Road_Class',
'Pedestrian_Crossing-Human_Control',
'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions',
'Special_Conditions_at_Site', 'Carriageway_Hazards', 'Urban_or_Rural_Area',
'Accident_Severity'
]]
NaN val percentages for filtering
print(acc.shape)
acc.isnull().mean()*100
(240171, 22)
Accident_Index 0.000000 LSOA_of_Accident_Location 5.062643 Longitude 0.034559 Latitude 0.034559 Date 0.000000 Day_of_Week 0.000000 Time 0.031644 1st_Road_Class 0.000000 Road_Type 0.000000 Speed_limit 0.000000 Junction_Detail 0.000000 Junction_Control 0.000000 2nd_Road_Class 0.000000 Pedestrian_Crossing-Human_Control 0.000000 Pedestrian_Crossing-Physical_Facilities 0.000000 Light_Conditions 0.000000 Weather_Conditions 0.000000 Road_Surface_Conditions 0.000000 Special_Conditions_at_Site 0.000000 Carriageway_Hazards 0.000000 Urban_or_Rural_Area 0.000000 Accident_Severity 0.000000 dtype: float64
#dropping nan vals
acc.dropna(inplace=True)
acc.reset_index(drop=True, inplace=True)
acc.head()
| Accident_Index | LSOA_of_Accident_Location | Longitude | Latitude | Date | Day_of_Week | Time | 1st_Road_Class | Road_Type | Speed_limit | Junction_Detail | Junction_Control | 2nd_Road_Class | Pedestrian_Crossing-Human_Control | Pedestrian_Crossing-Physical_Facilities | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Special_Conditions_at_Site | Carriageway_Hazards | Urban_or_Rural_Area | Accident_Severity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | E01000854 | -0.139737 | 51.524587 | 01/01/2018 | 2 | 01:30 | 3 | 3 | 30 | 0 | -1 | -1 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 1 | 3 |
| 1 | 2018010080973 | E01003531 | 0.046471 | 51.539651 | 01/01/2018 | 2 | 00:50 | 4 | 6 | 30 | 2 | 4 | 6 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 1 | 3 |
| 2 | 2018010080974 | E01002723 | -0.102474 | 51.529746 | 01/01/2018 | 2 | 00:45 | 3 | 6 | 20 | 6 | 4 | 5 | 0 | 5 | 4 | 1 | 1 | 0 | 0 | 1 | 3 |
| 3 | 2018010080981 | E01003492 | 0.037828 | 51.530179 | 01/01/2018 | 2 | 03:00 | 4 | 3 | 30 | 7 | 2 | 3 | 0 | 5 | 4 | 2 | 2 | 0 | 0 | 1 | 2 |
| 4 | 2018010080982 | E01001682 | 0.065781 | 51.469258 | 01/01/2018 | 2 | 02:20 | 3 | 6 | 30 | 0 | -1 | -1 | 0 | 0 | 4 | 1 | 2 | 0 | 0 | 1 | 2 |
keys = []
cols = [
'Day of Week', '1st Road Class', 'Road Type', 'Speed Limit', 'Junction Detail',
'Junction Control', '2nd Road Class', 'Ped Cross - Human',
'Ped Cross - Physical', 'Light Conditions',
'Weather', 'Road Surface',
'Special Conditions at Site', 'Carriageway Hazards', 'Urban Rural',
'Accident Severity'
]
for name in cols:
keys.append(pd.read_excel(f'./data/keys/variable lookup_{name}.xls'))
acc.columns
Index(['Accident_Index', 'LSOA_of_Accident_Location', 'Longitude', 'Latitude',
'Date', 'Day_of_Week', 'Time', '1st_Road_Class', 'Road_Type',
'Speed_limit', 'Junction_Detail', 'Junction_Control', '2nd_Road_Class',
'Pedestrian_Crossing-Human_Control',
'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions',
'Special_Conditions_at_Site', 'Carriageway_Hazards',
'Urban_or_Rural_Area', 'Accident_Severity'],
dtype='object')
repl_cols = [
'Day_of_Week', '1st_Road_Class', 'Road_Type', 'Speed_limit', 'Junction_Detail',
'Junction_Control', '2nd_Road_Class', 'Pedestrian_Crossing-Human_Control',
'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions',
'Special_Conditions_at_Site', 'Carriageway_Hazards', 'Urban_or_Rural_Area',
'Accident_Severity'
]
for i in range(len(repl_cols)):
acc = acc.replace({
repl_cols[i]:
dict([(i, a) for i, a in zip(keys[i]['code'], keys[i]['label'])])
})
acc.head()
| Accident_Index | LSOA_of_Accident_Location | Longitude | Latitude | Date | Day_of_Week | Time | 1st_Road_Class | Road_Type | Speed_limit | Junction_Detail | Junction_Control | 2nd_Road_Class | Pedestrian_Crossing-Human_Control | Pedestrian_Crossing-Physical_Facilities | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Special_Conditions_at_Site | Carriageway_Hazards | Urban_or_Rural_Area | Accident_Severity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | E01000854 | -0.139737 | 51.524587 | 01/01/2018 | Monday | 01:30 | A | Dual carriageway | 30 MPH | Not at junction or within 20 metres | Data missing or out of range | -1 | None within 50 metres | No physical crossing facilities within 50 metres | Darkness - lights lit | Fine no high winds | Dry | None | None | Urban | Slight |
| 1 | 2018010080973 | E01003531 | 0.046471 | 51.539651 | 01/01/2018 | Monday | 00:50 | B | Single carriageway | 30 MPH | Mini-roundabout | Give way or uncontrolled | Unclassified | None within 50 metres | No physical crossing facilities within 50 metres | Darkness - lights lit | Fine no high winds | Dry | None | None | Urban | Slight |
| 2 | 2018010080974 | E01002723 | -0.102474 | 51.529746 | 01/01/2018 | Monday | 00:45 | A | Single carriageway | 20 MPH | Crossroads | Give way or uncontrolled | C | None within 50 metres | Pedestrian phase at traffic signal junction | Darkness - lights lit | Fine no high winds | Dry | None | None | Urban | Slight |
| 3 | 2018010080981 | E01003492 | 0.037828 | 51.530179 | 01/01/2018 | Monday | 03:00 | B | Dual carriageway | 30 MPH | More than 4 arms (not roundabout) | Auto traffic signal | A | None within 50 metres | Pedestrian phase at traffic signal junction | Darkness - lights lit | Raining no high winds | Wet or damp | None | None | Urban | Serious |
| 4 | 2018010080982 | E01001682 | 0.065781 | 51.469258 | 01/01/2018 | Monday | 02:20 | A | Single carriageway | 30 MPH | Not at junction or within 20 metres | Data missing or out of range | -1 | None within 50 metres | No physical crossing facilities within 50 metres | Darkness - lights lit | Fine no high winds | Wet or damp | None | None | Urban | Serious |
for col in acc.columns[7:]:
print(col, '\n', acc[col].value_counts(), '\n')
1st_Road_Class A 100283 Unclassified 79837 B 27024 C 12699 Motorway 7474 A(M) 624 Name: 1st_Road_Class, dtype: int64 Road_Type Single carriageway 164413 Dual carriageway 36118 Roundabout 13811 One way street 5844 Unknown 4446 Slip road 3309 Name: Road_Type, dtype: int64 Speed_limit 30 MPH 136912 60 MPH 26687 20 MPH 21263 40 MPH 19540 70 MPH 14154 50 MPH 9306 Data missing or out of range 79 Name: Speed_limit, dtype: int64 Junction_Detail Not at junction or within 20 metres 95101 T or staggered junction 66628 Crossroads 21643 Roundabout 18296 Other junction 9567 Private drive or entrance 5283 Mini-roundabout 3176 Slip road 3158 More than 4 arms (not roundabout) 2926 Data missing or out of range 2163 Name: Junction_Detail, dtype: int64 Junction_Control Data missing or out of range 101221 Give way or uncontrolled 99142 Auto traffic signal 25323 Stop sign 1446 Authorised person 805 Not at junction or within 20 metres 4 Name: Junction_Control, dtype: int64 2nd_Road_Class -1 95280 Unclassified 91100 A 23710 B 8887 C 7736 Motorway 1093 A(M) 135 Name: 2nd_Road_Class, dtype: int64 Pedestrian_Crossing-Human_Control None within 50 metres 218087 Data missing or out of range 6783 Control by other authorised person 2239 Control by school crossing patrol 832 Name: Pedestrian_Crossing-Human_Control, dtype: int64 Pedestrian_Crossing-Physical_Facilities No physical crossing facilities within 50 metres 174985 Pedestrian phase at traffic signal junction 18356 Pelican, puffin, toucan or similar non-junction pedestrian light crossing 12964 Zebra 9191 Data missing or out of range 6074 Central refuge 5862 Footbridge or subway 509 Name: Pedestrian_Crossing-Physical_Facilities, dtype: int64 Light_Conditions Daylight 162950 Darkness - lights lit 47075 Darkness - no lighting 11227 Darkness - lighting unknown 5025 Darkness - lights unlit 1664 Name: Light_Conditions, dtype: int64 Weather_Conditions Fine no high winds 182273 Raining no high winds 26294 Unknown 6883 Other 5429 Raining + high winds 2443 Fine + high winds 2186 Snowing no high winds 1244 Fog or mist 792 Snowing + high winds 397 Name: Weather_Conditions, dtype: int64 Road_Surface_Conditions Dry 165209 Wet or damp 56469 Data missing or out of range 2542 Frost or ice 2290 Snow 1161 Flood over 3cm. deep 270 Name: Road_Surface_Conditions, dtype: int64 Special_Conditions_at_Site None 219760 Data missing or out of range 3332 Roadworks 2541 Mud 543 Auto traffic signal - out 534 Road surface defective 481 Road sign or marking defective or obscured 360 Oil or diesel 314 Auto signal part defective 76 Name: Special_Conditions_at_Site, dtype: int64 Carriageway_Hazards None 221125 Data missing or out of range 2871 Other object on road 1976 Any animal in carriageway (except ridden horse) 640 Vehicle load on road 515 Pedestrian in carriageway - not injured 426 Previous accident 388 Name: Carriageway_Hazards, dtype: int64 Urban_or_Rural_Area Urban 154682 Rural 73259 Name: Urban_or_Rural_Area, dtype: int64 Accident_Severity Slight 181445 Serious 43478 Fatal 3018 Name: Accident_Severity, dtype: int64
# Majorly single valued thus uninformative
acc = acc.drop(columns=[
'Junction_Control', '2nd_Road_Class', 'Pedestrian_Crossing-Human_Control',
'Pedestrian_Crossing-Physical_Facilities', 'Special_Conditions_at_Site',
'Carriageway_Hazards'
])
# Adding unknown to mode of data of R.V.
acc = acc.replace({
'1st_Road_Class': {
'A(M)': 'Motorway'
},
'Road_Type': {
'Unknown': 'Single carriageway'
},
'Speed_limit': {
'Data missing or out of range': '30 MPH'
},
'Junction_Detail': {
'Data missing or out of range': 'Not at junction or within 20 metres'
},
'Weather_Conditions': {
'Unknown': 'Fine no high winds'
},
'Road_Surface_Conditions': {
'Data missing or out of range': 'Dry'
}
})
In order to understand and investigate the correlation between the Roads-related factors and Accident severity, the classes, types and conditions of roads are studied in conjunction with the severity of the events.
def P(acc, col, cond):
return pd.DataFrame(
acc.groupby(cond)[col].value_counts(
normalize=True).unstack()).reset_index()
def chart_it(prob):
df = prob.melt(id_vars = prob.columns[0], value_vars = prob.columns[1:])
fig = px.bar(df, x=prob.columns[0], y="value",
color=prob.columns.name, barmode='group',
height=400)
fig.show()
acc['1st_Road_Class'].value_counts()
A 100283 Unclassified 79837 B 27024 C 12699 Motorway 8098 Name: 1st_Road_Class, dtype: int64
road = P(acc, 'Accident_Severity', '1st_Road_Class')
road
| Accident_Severity | 1st_Road_Class | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | A | 0.016473 | 0.189484 | 0.794043 |
| 1 | B | 0.014543 | 0.209702 | 0.775755 |
| 2 | C | 0.010395 | 0.177416 | 0.812190 |
| 3 | Motorway | 0.021363 | 0.153495 | 0.825142 |
| 4 | Unclassified | 0.008367 | 0.191803 | 0.799830 |
chart_it(road)
road = P(acc, '1st_Road_Class', 'Accident_Severity')
road
| 1st_Road_Class | Accident_Severity | A | B | C | Motorway | Unclassified |
|---|---|---|---|---|---|---|
| 0 | Fatal | 0.547382 | 0.130219 | 0.043738 | 0.057323 | 0.221339 |
| 1 | Serious | 0.437049 | 0.130342 | 0.051819 | 0.028589 | 0.352201 |
| 2 | Slight | 0.438860 | 0.115539 | 0.056844 | 0.036827 | 0.351930 |
chart_it(road)
In order to comment on the risk of traveling on a particular road, consider the P(Accident_Severity/1st_Road_Class) - Probability of Accident Severity given a Road Class, which highlights the investigative nature of the study by pointing towards the probability of the effect, given the cause. Thus, Motorways are the most prone to fatalities (2.1%) as compared to its counterparts.
Fatal accidents occur in greater frequency on A roads.
On the other hand, after a Bayesian treatment of the value counts of the same random variables, it is interesting to notice that the probabilities of P(1st_Road_Class/Accident_Severity) indicating probability of cause given effect turn out to be in the same proportion. This self-balancing is due to the extreme imbalance in the value counts of the Accident_Severity random variable. Even after random sampling to get equal number of Road class values, the extreme imbalance in Fatal, Severe and Slight variables causes the probabilities to skew in the direction of Slight accidents, irrespective of the feature variable. This must be handled at the time of model training.
l = []; equi = pd.DataFrame()
for val in (acc['1st_Road_Class'].unique()):
a = acc[acc['1st_Road_Class'] == val]
a = a.sample(n=1000)
l.append(a)
equi = l[0]
for df in l[1:]:
equi = pd.concat([equi, df])
equi['1st_Road_Class'].value_counts()
A 1000 B 1000 Unclassified 1000 C 1000 Motorway 1000 Name: 1st_Road_Class, dtype: int64
road = P(equi, 'Accident_Severity', '1st_Road_Class')
road
| Accident_Severity | 1st_Road_Class | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | A | 0.008 | 0.190 | 0.802 |
| 1 | B | 0.016 | 0.216 | 0.768 |
| 2 | C | 0.013 | 0.177 | 0.810 |
| 3 | Motorway | 0.021 | 0.148 | 0.831 |
| 4 | Unclassified | 0.009 | 0.185 | 0.806 |
road = P(equi, '1st_Road_Class', 'Accident_Severity')
road
| 1st_Road_Class | Accident_Severity | A | B | C | Motorway | Unclassified |
|---|---|---|---|---|---|---|
| 0 | Fatal | 0.119403 | 0.238806 | 0.194030 | 0.313433 | 0.134328 |
| 1 | Serious | 0.207424 | 0.235808 | 0.193231 | 0.161572 | 0.201965 |
| 2 | Slight | 0.199651 | 0.191187 | 0.201643 | 0.206871 | 0.200647 |
acc['Road_Type'].value_counts()
Single carriageway 168859 Dual carriageway 36118 Roundabout 13811 One way street 5844 Slip road 3309 Name: Road_Type, dtype: int64
rtype = P(acc, 'Accident_Severity', 'Road_Type')
rtype
| Accident_Severity | Road_Type | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | Dual carriageway | 0.017360 | 0.173653 | 0.808987 |
| 1 | One way street | 0.006160 | 0.156742 | 0.837098 |
| 2 | Roundabout | 0.003403 | 0.134748 | 0.861849 |
| 3 | Single carriageway | 0.013455 | 0.201156 | 0.785389 |
| 4 | Slip road | 0.010879 | 0.139619 | 0.849501 |
rtype = P(acc, 'Road_Type', 'Accident_Severity')
rtype
| Road_Type | Accident_Severity | Dual carriageway | One way street | Roundabout | Single carriageway | Slip road |
|---|---|---|---|---|---|---|
| 0 | Fatal | 0.207753 | 0.011928 | 0.015573 | 0.752816 | 0.011928 |
| 1 | Serious | 0.144257 | 0.021068 | 0.042803 | 0.781246 | 0.010626 |
| 2 | Slight | 0.161035 | 0.026961 | 0.065601 | 0.730910 | 0.015492 |
Dual carriageways are more prone to fatalities than its counterparts (17%).
speed = P(acc, 'Accident_Severity', 'Speed_limit')
speed
| Accident_Severity | Speed_limit | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | 20 MPH | 0.004374 | 0.157080 | 0.838546 |
| 1 | 30 MPH | 0.007643 | 0.180092 | 0.812265 |
| 2 | 40 MPH | 0.017042 | 0.207523 | 0.775435 |
| 3 | 50 MPH | 0.026220 | 0.205459 | 0.768322 |
| 4 | 60 MPH | 0.034511 | 0.261401 | 0.704088 |
| 5 | 70 MPH | 0.026848 | 0.178324 | 0.794828 |
speed_hist = pd.DataFrame(acc['Speed_limit'].value_counts()).reset_index()
speed_hist.sort_values(by=['index'], inplace=True)
fig = px.histogram(speed_hist, x='index', y='Speed_limit')
fig.show()
Notice that 60MPH speed limit roads have higher probability of fatal accidents, even though 30MPH roads have the highest frequency of accidents. This can be explained by the safe assumption that the average vehicle speeds have a direct positive correlation with the speed limit of the road. But 70MPH roads have lesser probability of fatal accidents and a significant jump in probability of slight accidents, maybe due to the fact that 70MPH roads are particularly built and engineered in a way that makes driving at high speed a more comfortable and safer experience.
junc = P(acc, 'Accident_Severity', 'Junction_Detail')
junc
| Accident_Severity | Junction_Detail | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | Crossroads | 0.008271 | 0.173312 | 0.818417 |
| 1 | Mini-roundabout | 0.002834 | 0.143577 | 0.853589 |
| 2 | More than 4 arms (not roundabout) | 0.005810 | 0.184211 | 0.809979 |
| 3 | Not at junction or within 20 metres | 0.019452 | 0.205194 | 0.775354 |
| 4 | Other junction | 0.010348 | 0.196822 | 0.792830 |
| 5 | Private drive or entrance | 0.008896 | 0.191936 | 0.799167 |
| 6 | Roundabout | 0.004974 | 0.138883 | 0.856143 |
| 7 | Slip road | 0.014883 | 0.151045 | 0.834072 |
| 8 | T or staggered junction | 0.009561 | 0.192997 | 0.797443 |
junc = P(acc, 'Junction_Detail', 'Accident_Severity')
display(junc)
chart_it(junc)
| Junction_Detail | Accident_Severity | Crossroads | Mini-roundabout | More than 4 arms (not roundabout) | Not at junction or within 20 metres | Other junction | Private drive or entrance | Roundabout | Slip road | T or staggered junction |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Fatal | 0.059311 | 0.002982 | 0.005633 | 0.626905 | 0.032803 | 0.015573 | 0.030152 | 0.015573 | 0.211067 |
| 1 | Serious | 0.086274 | 0.010488 | 0.012397 | 0.459037 | 0.043309 | 0.023322 | 0.058443 | 0.010971 | 0.295759 |
| 2 | Slight | 0.097622 | 0.014941 | 0.013062 | 0.415630 | 0.041803 | 0.023269 | 0.086329 | 0.014517 | 0.292827 |
Clearly, roads having no junction within 20 meters have the most number of accidents and are the riskiest too, with respect to the probability of having a fatality. The probability of a slight accident on these kind of roads is the least when compared to its counterparts too.
light = P(acc, 'Accident_Severity', 'Light_Conditions')
light
| Accident_Severity | Light_Conditions | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | Darkness - lighting unknown | 0.010945 | 0.140100 | 0.848955 |
| 1 | Darkness - lights lit | 0.014041 | 0.202953 | 0.783006 |
| 2 | Darkness - lights unlit | 0.024639 | 0.213942 | 0.761418 |
| 3 | Darkness - no lighting | 0.046851 | 0.266144 | 0.687005 |
| 4 | Daylight | 0.010647 | 0.183345 | 0.806008 |
weather = P(acc, 'Accident_Severity', 'Weather_Conditions')
weather
| Accident_Severity | Weather_Conditions | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | Fine + high winds | 0.023788 | 0.209058 | 0.767155 |
| 1 | Fine no high winds | 0.013259 | 0.192777 | 0.793964 |
| 2 | Fog or mist | 0.049242 | 0.202020 | 0.748737 |
| 3 | Other | 0.007552 | 0.147909 | 0.844539 |
| 4 | Raining + high winds | 0.023741 | 0.212034 | 0.764224 |
| 5 | Raining no high winds | 0.011447 | 0.183806 | 0.804746 |
| 6 | Snowing + high winds | 0.005038 | 0.148615 | 0.846348 |
| 7 | Snowing no high winds | 0.013666 | 0.147106 | 0.839228 |
For obvious reasons, conditions with No lighting and climatic conditions of high winds and rains have the highest probability of fatalities and the lowest probabilities of slight accidents
time = acc[['Date', 'Accident_Severity']]
time['Date'] = time['Date'].apply(lambda x: datetime.strptime(x, '%d/%m/%Y'))
time = pd.DataFrame(time.groupby('Date')['Accident_Severity'].value_counts()).unstack().unstack()
time = pd.DataFrame(time['Accident_Severity']).reset_index()
time.replace(np.nan, 0, inplace=True)
time
| Accident_Severity | Date | 0 | |
|---|---|---|---|
| 0 | Fatal | 2018-01-01 | 4.0 |
| 1 | Fatal | 2018-01-02 | 3.0 |
| 2 | Fatal | 2018-01-03 | 3.0 |
| 3 | Fatal | 2018-01-04 | 6.0 |
| 4 | Fatal | 2018-01-05 | 4.0 |
| 5 | Fatal | 2018-01-06 | 1.0 |
| 6 | Fatal | 2018-01-07 | 2.0 |
| 7 | Fatal | 2018-01-08 | 0.0 |
| 8 | Fatal | 2018-01-09 | 2.0 |
| 9 | Fatal | 2018-01-10 | 5.0 |
| 10 | Fatal | 2018-01-11 | 7.0 |
| 11 | Fatal | 2018-01-12 | 3.0 |
| 12 | Fatal | 2018-01-13 | 6.0 |
| 13 | Fatal | 2018-01-14 | 4.0 |
| 14 | Fatal | 2018-01-15 | 2.0 |
| 15 | Fatal | 2018-01-16 | 2.0 |
| 16 | Fatal | 2018-01-17 | 5.0 |
| 17 | Fatal | 2018-01-18 | 2.0 |
| 18 | Fatal | 2018-01-19 | 4.0 |
| 19 | Fatal | 2018-01-20 | 8.0 |
| 20 | Fatal | 2018-01-21 | 5.0 |
| 21 | Fatal | 2018-01-22 | 5.0 |
| 22 | Fatal | 2018-01-23 | 3.0 |
| 23 | Fatal | 2018-01-24 | 1.0 |
| 24 | Fatal | 2018-01-25 | 7.0 |
| 25 | Fatal | 2018-01-26 | 5.0 |
| 26 | Fatal | 2018-01-27 | 3.0 |
| 27 | Fatal | 2018-01-28 | 5.0 |
| 28 | Fatal | 2018-01-29 | 5.0 |
| 29 | Fatal | 2018-01-30 | 1.0 |
| 30 | Fatal | 2018-01-31 | 1.0 |
| 31 | Fatal | 2018-02-01 | 5.0 |
| 32 | Fatal | 2018-02-02 | 6.0 |
| 33 | Fatal | 2018-02-03 | 5.0 |
| 34 | Fatal | 2018-02-04 | 2.0 |
| 35 | Fatal | 2018-02-05 | 2.0 |
| 36 | Fatal | 2018-02-06 | 6.0 |
| 37 | Fatal | 2018-02-07 | 3.0 |
| 38 | Fatal | 2018-02-08 | 0.0 |
| 39 | Fatal | 2018-02-09 | 3.0 |
| 40 | Fatal | 2018-02-10 | 1.0 |
| 41 | Fatal | 2018-02-11 | 4.0 |
| 42 | Fatal | 2018-02-12 | 7.0 |
| 43 | Fatal | 2018-02-13 | 4.0 |
| 44 | Fatal | 2018-02-14 | 2.0 |
| 45 | Fatal | 2018-02-15 | 5.0 |
| 46 | Fatal | 2018-02-16 | 2.0 |
| 47 | Fatal | 2018-02-17 | 6.0 |
| 48 | Fatal | 2018-02-18 | 2.0 |
| 49 | Fatal | 2018-02-19 | 6.0 |
| 50 | Fatal | 2018-02-20 | 5.0 |
| 51 | Fatal | 2018-02-21 | 2.0 |
| 52 | Fatal | 2018-02-22 | 1.0 |
| 53 | Fatal | 2018-02-23 | 3.0 |
| 54 | Fatal | 2018-02-24 | 8.0 |
| 55 | Fatal | 2018-02-25 | 3.0 |
| 56 | Fatal | 2018-02-26 | 5.0 |
| 57 | Fatal | 2018-02-27 | 7.0 |
| 58 | Fatal | 2018-02-28 | 3.0 |
| 59 | Fatal | 2018-03-01 | 4.0 |
| 60 | Fatal | 2018-03-02 | 1.0 |
| 61 | Fatal | 2018-03-03 | 1.0 |
| 62 | Fatal | 2018-03-04 | 2.0 |
| 63 | Fatal | 2018-03-05 | 4.0 |
| 64 | Fatal | 2018-03-06 | 5.0 |
| 65 | Fatal | 2018-03-07 | 5.0 |
| 66 | Fatal | 2018-03-08 | 3.0 |
| 67 | Fatal | 2018-03-09 | 1.0 |
| 68 | Fatal | 2018-03-10 | 3.0 |
| 69 | Fatal | 2018-03-11 | 6.0 |
| 70 | Fatal | 2018-03-12 | 3.0 |
| 71 | Fatal | 2018-03-13 | 6.0 |
| 72 | Fatal | 2018-03-14 | 3.0 |
| 73 | Fatal | 2018-03-15 | 1.0 |
| 74 | Fatal | 2018-03-16 | 6.0 |
| 75 | Fatal | 2018-03-17 | 5.0 |
| 76 | Fatal | 2018-03-18 | 2.0 |
| 77 | Fatal | 2018-03-19 | 3.0 |
| 78 | Fatal | 2018-03-20 | 2.0 |
| 79 | Fatal | 2018-03-21 | 2.0 |
| 80 | Fatal | 2018-03-22 | 4.0 |
| 81 | Fatal | 2018-03-23 | 5.0 |
| 82 | Fatal | 2018-03-24 | 2.0 |
| 83 | Fatal | 2018-03-25 | 5.0 |
| 84 | Fatal | 2018-03-26 | 4.0 |
| 85 | Fatal | 2018-03-27 | 3.0 |
| 86 | Fatal | 2018-03-28 | 5.0 |
| 87 | Fatal | 2018-03-29 | 2.0 |
| 88 | Fatal | 2018-03-30 | 4.0 |
| 89 | Fatal | 2018-03-31 | 0.0 |
| 90 | Fatal | 2018-04-01 | 6.0 |
| 91 | Fatal | 2018-04-02 | 6.0 |
| 92 | Fatal | 2018-04-03 | 6.0 |
| 93 | Fatal | 2018-04-04 | 0.0 |
| 94 | Fatal | 2018-04-05 | 5.0 |
| 95 | Fatal | 2018-04-06 | 3.0 |
| 96 | Fatal | 2018-04-07 | 4.0 |
| 97 | Fatal | 2018-04-08 | 5.0 |
| 98 | Fatal | 2018-04-09 | 6.0 |
| 99 | Fatal | 2018-04-10 | 1.0 |
| 100 | Fatal | 2018-04-11 | 2.0 |
| 101 | Fatal | 2018-04-12 | 1.0 |
| 102 | Fatal | 2018-04-13 | 3.0 |
| 103 | Fatal | 2018-04-14 | 3.0 |
| 104 | Fatal | 2018-04-15 | 6.0 |
| 105 | Fatal | 2018-04-16 | 3.0 |
| 106 | Fatal | 2018-04-17 | 2.0 |
| 107 | Fatal | 2018-04-18 | 6.0 |
| 108 | Fatal | 2018-04-19 | 4.0 |
| 109 | Fatal | 2018-04-20 | 3.0 |
| 110 | Fatal | 2018-04-21 | 4.0 |
| 111 | Fatal | 2018-04-22 | 3.0 |
| 112 | Fatal | 2018-04-23 | 4.0 |
| 113 | Fatal | 2018-04-24 | 2.0 |
| 114 | Fatal | 2018-04-25 | 3.0 |
| 115 | Fatal | 2018-04-26 | 5.0 |
| 116 | Fatal | 2018-04-27 | 2.0 |
| 117 | Fatal | 2018-04-28 | 4.0 |
| 118 | Fatal | 2018-04-29 | 7.0 |
| 119 | Fatal | 2018-04-30 | 4.0 |
| 120 | Fatal | 2018-05-01 | 4.0 |
| 121 | Fatal | 2018-05-02 | 5.0 |
| 122 | Fatal | 2018-05-03 | 4.0 |
| 123 | Fatal | 2018-05-04 | 7.0 |
| 124 | Fatal | 2018-05-05 | 5.0 |
| 125 | Fatal | 2018-05-06 | 6.0 |
| 126 | Fatal | 2018-05-07 | 6.0 |
| 127 | Fatal | 2018-05-08 | 0.0 |
| 128 | Fatal | 2018-05-09 | 3.0 |
| 129 | Fatal | 2018-05-10 | 3.0 |
| 130 | Fatal | 2018-05-11 | 2.0 |
| 131 | Fatal | 2018-05-12 | 8.0 |
| 132 | Fatal | 2018-05-13 | 4.0 |
| 133 | Fatal | 2018-05-14 | 4.0 |
| 134 | Fatal | 2018-05-15 | 6.0 |
| 135 | Fatal | 2018-05-16 | 3.0 |
| 136 | Fatal | 2018-05-17 | 4.0 |
| 137 | Fatal | 2018-05-18 | 9.0 |
| 138 | Fatal | 2018-05-19 | 5.0 |
| 139 | Fatal | 2018-05-20 | 3.0 |
| 140 | Fatal | 2018-05-21 | 9.0 |
| 141 | Fatal | 2018-05-22 | 4.0 |
| 142 | Fatal | 2018-05-23 | 2.0 |
| 143 | Fatal | 2018-05-24 | 3.0 |
| 144 | Fatal | 2018-05-25 | 2.0 |
| 145 | Fatal | 2018-05-26 | 4.0 |
| 146 | Fatal | 2018-05-27 | 2.0 |
| 147 | Fatal | 2018-05-28 | 6.0 |
| 148 | Fatal | 2018-05-29 | 6.0 |
| 149 | Fatal | 2018-05-30 | 5.0 |
| 150 | Fatal | 2018-05-31 | 3.0 |
| 151 | Fatal | 2018-06-01 | 1.0 |
| 152 | Fatal | 2018-06-02 | 2.0 |
| 153 | Fatal | 2018-06-03 | 6.0 |
| 154 | Fatal | 2018-06-04 | 6.0 |
| 155 | Fatal | 2018-06-05 | 5.0 |
| 156 | Fatal | 2018-06-06 | 4.0 |
| 157 | Fatal | 2018-06-07 | 9.0 |
| 158 | Fatal | 2018-06-08 | 1.0 |
| 159 | Fatal | 2018-06-09 | 2.0 |
| 160 | Fatal | 2018-06-10 | 1.0 |
| 161 | Fatal | 2018-06-11 | 3.0 |
| 162 | Fatal | 2018-06-12 | 4.0 |
| 163 | Fatal | 2018-06-13 | 3.0 |
| 164 | Fatal | 2018-06-14 | 0.0 |
| 165 | Fatal | 2018-06-15 | 2.0 |
| 166 | Fatal | 2018-06-16 | 4.0 |
| 167 | Fatal | 2018-06-17 | 2.0 |
| 168 | Fatal | 2018-06-18 | 7.0 |
| 169 | Fatal | 2018-06-19 | 3.0 |
| 170 | Fatal | 2018-06-20 | 5.0 |
| 171 | Fatal | 2018-06-21 | 4.0 |
| 172 | Fatal | 2018-06-22 | 7.0 |
| 173 | Fatal | 2018-06-23 | 6.0 |
| 174 | Fatal | 2018-06-24 | 3.0 |
| 175 | Fatal | 2018-06-25 | 5.0 |
| 176 | Fatal | 2018-06-26 | 2.0 |
| 177 | Fatal | 2018-06-27 | 2.0 |
| 178 | Fatal | 2018-06-28 | 4.0 |
| 179 | Fatal | 2018-06-29 | 5.0 |
| 180 | Fatal | 2018-06-30 | 5.0 |
| 181 | Fatal | 2018-07-01 | 6.0 |
| 182 | Fatal | 2018-07-02 | 4.0 |
| 183 | Fatal | 2018-07-03 | 7.0 |
| 184 | Fatal | 2018-07-04 | 5.0 |
| 185 | Fatal | 2018-07-05 | 6.0 |
| 186 | Fatal | 2018-07-06 | 3.0 |
| 187 | Fatal | 2018-07-07 | 7.0 |
| 188 | Fatal | 2018-07-08 | 3.0 |
| 189 | Fatal | 2018-07-09 | 5.0 |
| 190 | Fatal | 2018-07-10 | 1.0 |
| 191 | Fatal | 2018-07-11 | 4.0 |
| 192 | Fatal | 2018-07-12 | 2.0 |
| 193 | Fatal | 2018-07-13 | 4.0 |
| 194 | Fatal | 2018-07-14 | 3.0 |
| 195 | Fatal | 2018-07-15 | 2.0 |
| 196 | Fatal | 2018-07-16 | 3.0 |
| 197 | Fatal | 2018-07-17 | 5.0 |
| 198 | Fatal | 2018-07-18 | 1.0 |
| 199 | Fatal | 2018-07-19 | 1.0 |
| 200 | Fatal | 2018-07-20 | 2.0 |
| 201 | Fatal | 2018-07-21 | 4.0 |
| 202 | Fatal | 2018-07-22 | 4.0 |
| 203 | Fatal | 2018-07-23 | 5.0 |
| 204 | Fatal | 2018-07-24 | 3.0 |
| 205 | Fatal | 2018-07-25 | 3.0 |
| 206 | Fatal | 2018-07-26 | 2.0 |
| 207 | Fatal | 2018-07-27 | 5.0 |
| 208 | Fatal | 2018-07-28 | 4.0 |
| 209 | Fatal | 2018-07-29 | 5.0 |
| 210 | Fatal | 2018-07-30 | 6.0 |
| 211 | Fatal | 2018-07-31 | 5.0 |
| 212 | Fatal | 2018-08-01 | 3.0 |
| 213 | Fatal | 2018-08-02 | 6.0 |
| 214 | Fatal | 2018-08-03 | 5.0 |
| 215 | Fatal | 2018-08-04 | 4.0 |
| 216 | Fatal | 2018-08-05 | 7.0 |
| 217 | Fatal | 2018-08-06 | 4.0 |
| 218 | Fatal | 2018-08-07 | 2.0 |
| 219 | Fatal | 2018-08-08 | 6.0 |
| 220 | Fatal | 2018-08-09 | 3.0 |
| 221 | Fatal | 2018-08-10 | 2.0 |
| 222 | Fatal | 2018-08-11 | 5.0 |
| 223 | Fatal | 2018-08-12 | 3.0 |
| 224 | Fatal | 2018-08-13 | 4.0 |
| 225 | Fatal | 2018-08-14 | 3.0 |
| 226 | Fatal | 2018-08-15 | 3.0 |
| 227 | Fatal | 2018-08-16 | 10.0 |
| 228 | Fatal | 2018-08-17 | 3.0 |
| 229 | Fatal | 2018-08-18 | 8.0 |
| 230 | Fatal | 2018-08-19 | 6.0 |
| 231 | Fatal | 2018-08-20 | 4.0 |
| 232 | Fatal | 2018-08-21 | 2.0 |
| 233 | Fatal | 2018-08-22 | 7.0 |
| 234 | Fatal | 2018-08-23 | 2.0 |
| 235 | Fatal | 2018-08-24 | 4.0 |
| 236 | Fatal | 2018-08-25 | 5.0 |
| 237 | Fatal | 2018-08-26 | 3.0 |
| 238 | Fatal | 2018-08-27 | 4.0 |
| 239 | Fatal | 2018-08-28 | 2.0 |
| 240 | Fatal | 2018-08-29 | 3.0 |
| 241 | Fatal | 2018-08-30 | 2.0 |
| 242 | Fatal | 2018-08-31 | 5.0 |
| 243 | Fatal | 2018-09-01 | 5.0 |
| 244 | Fatal | 2018-09-02 | 5.0 |
| 245 | Fatal | 2018-09-03 | 2.0 |
| 246 | Fatal | 2018-09-04 | 1.0 |
| 247 | Fatal | 2018-09-05 | 1.0 |
| 248 | Fatal | 2018-09-06 | 4.0 |
| 249 | Fatal | 2018-09-07 | 2.0 |
| 250 | Fatal | 2018-09-08 | 4.0 |
| 251 | Fatal | 2018-09-09 | 10.0 |
| 252 | Fatal | 2018-09-10 | 6.0 |
| 253 | Fatal | 2018-09-11 | 3.0 |
| 254 | Fatal | 2018-09-12 | 1.0 |
| 255 | Fatal | 2018-09-13 | 5.0 |
| 256 | Fatal | 2018-09-14 | 7.0 |
| 257 | Fatal | 2018-09-15 | 6.0 |
| 258 | Fatal | 2018-09-16 | 4.0 |
| 259 | Fatal | 2018-09-17 | 2.0 |
| 260 | Fatal | 2018-09-18 | 4.0 |
| 261 | Fatal | 2018-09-19 | 3.0 |
| 262 | Fatal | 2018-09-20 | 2.0 |
| 263 | Fatal | 2018-09-21 | 3.0 |
| 264 | Fatal | 2018-09-22 | 7.0 |
| 265 | Fatal | 2018-09-23 | 3.0 |
| 266 | Fatal | 2018-09-24 | 7.0 |
| 267 | Fatal | 2018-09-25 | 2.0 |
| 268 | Fatal | 2018-09-26 | 1.0 |
| 269 | Fatal | 2018-09-27 | 9.0 |
| 270 | Fatal | 2018-09-28 | 10.0 |
| 271 | Fatal | 2018-09-29 | 8.0 |
| 272 | Fatal | 2018-09-30 | 5.0 |
| 273 | Fatal | 2018-10-01 | 5.0 |
| 274 | Fatal | 2018-10-02 | 4.0 |
| 275 | Fatal | 2018-10-03 | 8.0 |
| 276 | Fatal | 2018-10-04 | 5.0 |
| 277 | Fatal | 2018-10-05 | 2.0 |
| 278 | Fatal | 2018-10-06 | 6.0 |
| 279 | Fatal | 2018-10-07 | 7.0 |
| 280 | Fatal | 2018-10-08 | 4.0 |
| 281 | Fatal | 2018-10-09 | 10.0 |
| 282 | Fatal | 2018-10-10 | 6.0 |
| 283 | Fatal | 2018-10-11 | 5.0 |
| 284 | Fatal | 2018-10-12 | 7.0 |
| 285 | Fatal | 2018-10-13 | 5.0 |
| 286 | Fatal | 2018-10-14 | 8.0 |
| 287 | Fatal | 2018-10-15 | 5.0 |
| 288 | Fatal | 2018-10-16 | 2.0 |
| 289 | Fatal | 2018-10-17 | 3.0 |
| 290 | Fatal | 2018-10-18 | 5.0 |
| 291 | Fatal | 2018-10-19 | 6.0 |
| 292 | Fatal | 2018-10-20 | 6.0 |
| 293 | Fatal | 2018-10-21 | 7.0 |
| 294 | Fatal | 2018-10-22 | 4.0 |
| 295 | Fatal | 2018-10-23 | 3.0 |
| 296 | Fatal | 2018-10-24 | 7.0 |
| 297 | Fatal | 2018-10-25 | 5.0 |
| 298 | Fatal | 2018-10-26 | 8.0 |
| 299 | Fatal | 2018-10-27 | 2.0 |
| 300 | Fatal | 2018-10-28 | 8.0 |
| 301 | Fatal | 2018-10-29 | 3.0 |
| 302 | Fatal | 2018-10-30 | 1.0 |
| 303 | Fatal | 2018-10-31 | 4.0 |
| 304 | Fatal | 2018-11-01 | 6.0 |
| 305 | Fatal | 2018-11-02 | 4.0 |
| 306 | Fatal | 2018-11-03 | 5.0 |
| 307 | Fatal | 2018-11-04 | 3.0 |
| 308 | Fatal | 2018-11-05 | 7.0 |
| 309 | Fatal | 2018-11-06 | 7.0 |
| 310 | Fatal | 2018-11-07 | 6.0 |
| 311 | Fatal | 2018-11-08 | 1.0 |
| 312 | Fatal | 2018-11-09 | 3.0 |
| 313 | Fatal | 2018-11-10 | 7.0 |
| 314 | Fatal | 2018-11-11 | 8.0 |
| 315 | Fatal | 2018-11-12 | 4.0 |
| 316 | Fatal | 2018-11-13 | 4.0 |
| 317 | Fatal | 2018-11-14 | 1.0 |
| 318 | Fatal | 2018-11-15 | 3.0 |
| 319 | Fatal | 2018-11-16 | 7.0 |
| 320 | Fatal | 2018-11-17 | 6.0 |
| 321 | Fatal | 2018-11-18 | 8.0 |
| 322 | Fatal | 2018-11-19 | 1.0 |
| 323 | Fatal | 2018-11-20 | 5.0 |
| 324 | Fatal | 2018-11-21 | 5.0 |
| 325 | Fatal | 2018-11-22 | 2.0 |
| 326 | Fatal | 2018-11-23 | 3.0 |
| 327 | Fatal | 2018-11-24 | 11.0 |
| 328 | Fatal | 2018-11-25 | 1.0 |
| 329 | Fatal | 2018-11-26 | 3.0 |
| 330 | Fatal | 2018-11-27 | 3.0 |
| 331 | Fatal | 2018-11-28 | 5.0 |
| 332 | Fatal | 2018-11-29 | 3.0 |
| 333 | Fatal | 2018-11-30 | 9.0 |
| 334 | Fatal | 2018-12-01 | 5.0 |
| 335 | Fatal | 2018-12-02 | 2.0 |
| 336 | Fatal | 2018-12-03 | 4.0 |
| 337 | Fatal | 2018-12-04 | 9.0 |
| 338 | Fatal | 2018-12-05 | 3.0 |
| 339 | Fatal | 2018-12-06 | 4.0 |
| 340 | Fatal | 2018-12-07 | 6.0 |
| 341 | Fatal | 2018-12-08 | 10.0 |
| 342 | Fatal | 2018-12-09 | 8.0 |
| 343 | Fatal | 2018-12-10 | 2.0 |
| 344 | Fatal | 2018-12-11 | 6.0 |
| 345 | Fatal | 2018-12-12 | 2.0 |
| 346 | Fatal | 2018-12-13 | 4.0 |
| 347 | Fatal | 2018-12-14 | 8.0 |
| 348 | Fatal | 2018-12-15 | 3.0 |
| 349 | Fatal | 2018-12-16 | 4.0 |
| 350 | Fatal | 2018-12-17 | 4.0 |
| 351 | Fatal | 2018-12-18 | 4.0 |
| 352 | Fatal | 2018-12-19 | 3.0 |
| 353 | Fatal | 2018-12-20 | 4.0 |
| 354 | Fatal | 2018-12-21 | 8.0 |
| 355 | Fatal | 2018-12-22 | 10.0 |
| 356 | Fatal | 2018-12-23 | 7.0 |
| 357 | Fatal | 2018-12-24 | 2.0 |
| 358 | Fatal | 2018-12-25 | 4.0 |
| 359 | Fatal | 2018-12-26 | 3.0 |
| 360 | Fatal | 2018-12-27 | 3.0 |
| 361 | Fatal | 2018-12-28 | 6.0 |
| 362 | Fatal | 2018-12-29 | 5.0 |
| 363 | Fatal | 2018-12-30 | 2.0 |
| 364 | Fatal | 2018-12-31 | 3.0 |
| 365 | Fatal | 2019-01-01 | 7.0 |
| 366 | Fatal | 2019-01-02 | 3.0 |
| 367 | Fatal | 2019-01-03 | 8.0 |
| 368 | Fatal | 2019-01-04 | 1.0 |
| 369 | Fatal | 2019-01-05 | 6.0 |
| 370 | Fatal | 2019-01-06 | 4.0 |
| 371 | Fatal | 2019-01-07 | 13.0 |
| 372 | Fatal | 2019-01-08 | 8.0 |
| 373 | Fatal | 2019-01-09 | 4.0 |
| 374 | Fatal | 2019-01-10 | 2.0 |
| 375 | Fatal | 2019-01-11 | 9.0 |
| 376 | Fatal | 2019-01-12 | 4.0 |
| 377 | Fatal | 2019-01-13 | 3.0 |
| 378 | Fatal | 2019-01-14 | 5.0 |
| 379 | Fatal | 2019-01-15 | 4.0 |
| 380 | Fatal | 2019-01-16 | 4.0 |
| 381 | Fatal | 2019-01-17 | 5.0 |
| 382 | Fatal | 2019-01-18 | 3.0 |
| 383 | Fatal | 2019-01-19 | 2.0 |
| 384 | Fatal | 2019-01-20 | 1.0 |
| 385 | Fatal | 2019-01-21 | 2.0 |
| 386 | Fatal | 2019-01-22 | 7.0 |
| 387 | Fatal | 2019-01-23 | 9.0 |
| 388 | Fatal | 2019-01-24 | 2.0 |
| 389 | Fatal | 2019-01-25 | 5.0 |
| 390 | Fatal | 2019-01-26 | 6.0 |
| 391 | Fatal | 2019-01-27 | 4.0 |
| 392 | Fatal | 2019-01-28 | 6.0 |
| 393 | Fatal | 2019-01-29 | 3.0 |
| 394 | Fatal | 2019-01-30 | 6.0 |
| 395 | Fatal | 2019-01-31 | 4.0 |
| 396 | Fatal | 2019-02-01 | 2.0 |
| 397 | Fatal | 2019-02-02 | 6.0 |
| 398 | Fatal | 2019-02-03 | 2.0 |
| 399 | Fatal | 2019-02-04 | 3.0 |
| 400 | Fatal | 2019-02-05 | 5.0 |
| 401 | Fatal | 2019-02-06 | 2.0 |
| 402 | Fatal | 2019-02-07 | 4.0 |
| 403 | Fatal | 2019-02-08 | 6.0 |
| 404 | Fatal | 2019-02-09 | 8.0 |
| 405 | Fatal | 2019-02-10 | 2.0 |
| 406 | Fatal | 2019-02-11 | 2.0 |
| 407 | Fatal | 2019-02-12 | 3.0 |
| 408 | Fatal | 2019-02-13 | 5.0 |
| 409 | Fatal | 2019-02-14 | 3.0 |
| 410 | Fatal | 2019-02-15 | 6.0 |
| 411 | Fatal | 2019-02-16 | 4.0 |
| 412 | Fatal | 2019-02-17 | 6.0 |
| 413 | Fatal | 2019-02-18 | 5.0 |
| 414 | Fatal | 2019-02-19 | 3.0 |
| 415 | Fatal | 2019-02-20 | 5.0 |
| 416 | Fatal | 2019-02-21 | 4.0 |
| 417 | Fatal | 2019-02-22 | 5.0 |
| 418 | Fatal | 2019-02-23 | 10.0 |
| 419 | Fatal | 2019-02-24 | 6.0 |
| 420 | Fatal | 2019-02-25 | 5.0 |
| 421 | Fatal | 2019-02-26 | 4.0 |
| 422 | Fatal | 2019-02-27 | 2.0 |
| 423 | Fatal | 2019-02-28 | 3.0 |
| 424 | Fatal | 2019-03-01 | 4.0 |
| 425 | Fatal | 2019-03-02 | 3.0 |
| 426 | Fatal | 2019-03-03 | 2.0 |
| 427 | Fatal | 2019-03-04 | 2.0 |
| 428 | Fatal | 2019-03-05 | 3.0 |
| 429 | Fatal | 2019-03-06 | 6.0 |
| 430 | Fatal | 2019-03-07 | 2.0 |
| 431 | Fatal | 2019-03-08 | 3.0 |
| 432 | Fatal | 2019-03-09 | 1.0 |
| 433 | Fatal | 2019-03-10 | 4.0 |
| 434 | Fatal | 2019-03-11 | 3.0 |
| 435 | Fatal | 2019-03-12 | 2.0 |
| 436 | Fatal | 2019-03-13 | 1.0 |
| 437 | Fatal | 2019-03-14 | 5.0 |
| 438 | Fatal | 2019-03-15 | 2.0 |
| 439 | Fatal | 2019-03-16 | 4.0 |
| 440 | Fatal | 2019-03-17 | 2.0 |
| 441 | Fatal | 2019-03-18 | 2.0 |
| 442 | Fatal | 2019-03-19 | 2.0 |
| 443 | Fatal | 2019-03-20 | 6.0 |
| 444 | Fatal | 2019-03-21 | 6.0 |
| 445 | Fatal | 2019-03-22 | 4.0 |
| 446 | Fatal | 2019-03-23 | 6.0 |
| 447 | Fatal | 2019-03-24 | 3.0 |
| 448 | Fatal | 2019-03-25 | 4.0 |
| 449 | Fatal | 2019-03-26 | 5.0 |
| 450 | Fatal | 2019-03-27 | 4.0 |
| 451 | Fatal | 2019-03-28 | 2.0 |
| 452 | Fatal | 2019-03-29 | 5.0 |
| 453 | Fatal | 2019-03-30 | 6.0 |
| 454 | Fatal | 2019-03-31 | 5.0 |
| 455 | Fatal | 2019-04-01 | 4.0 |
| 456 | Fatal | 2019-04-02 | 4.0 |
| 457 | Fatal | 2019-04-03 | 7.0 |
| 458 | Fatal | 2019-04-04 | 3.0 |
| 459 | Fatal | 2019-04-05 | 1.0 |
| 460 | Fatal | 2019-04-06 | 4.0 |
| 461 | Fatal | 2019-04-07 | 5.0 |
| 462 | Fatal | 2019-04-08 | 3.0 |
| 463 | Fatal | 2019-04-09 | 1.0 |
| 464 | Fatal | 2019-04-10 | 7.0 |
| 465 | Fatal | 2019-04-11 | 6.0 |
| 466 | Fatal | 2019-04-12 | 3.0 |
| 467 | Fatal | 2019-04-13 | 4.0 |
| 468 | Fatal | 2019-04-14 | 7.0 |
| 469 | Fatal | 2019-04-15 | 3.0 |
| 470 | Fatal | 2019-04-16 | 8.0 |
| 471 | Fatal | 2019-04-17 | 5.0 |
| 472 | Fatal | 2019-04-18 | 3.0 |
| 473 | Fatal | 2019-04-19 | 6.0 |
| 474 | Fatal | 2019-04-20 | 9.0 |
| 475 | Fatal | 2019-04-21 | 8.0 |
| 476 | Fatal | 2019-04-22 | 4.0 |
| 477 | Fatal | 2019-04-23 | 5.0 |
| 478 | Fatal | 2019-04-24 | 3.0 |
| 479 | Fatal | 2019-04-25 | 2.0 |
| 480 | Fatal | 2019-04-26 | 3.0 |
| 481 | Fatal | 2019-04-27 | 1.0 |
| 482 | Fatal | 2019-04-28 | 2.0 |
| 483 | Fatal | 2019-04-29 | 3.0 |
| 484 | Fatal | 2019-04-30 | 3.0 |
| 485 | Fatal | 2019-05-01 | 2.0 |
| 486 | Fatal | 2019-05-02 | 3.0 |
| 487 | Fatal | 2019-05-03 | 4.0 |
| 488 | Fatal | 2019-05-04 | 2.0 |
| 489 | Fatal | 2019-05-05 | 4.0 |
| 490 | Fatal | 2019-05-06 | 4.0 |
| 491 | Fatal | 2019-05-07 | 2.0 |
| 492 | Fatal | 2019-05-08 | 4.0 |
| 493 | Fatal | 2019-05-09 | 3.0 |
| 494 | Fatal | 2019-05-10 | 4.0 |
| 495 | Fatal | 2019-05-11 | 4.0 |
| 496 | Fatal | 2019-05-12 | 5.0 |
| 497 | Fatal | 2019-05-13 | 3.0 |
| 498 | Fatal | 2019-05-14 | 3.0 |
| 499 | Fatal | 2019-05-15 | 5.0 |
| 500 | Fatal | 2019-05-16 | 4.0 |
| 501 | Fatal | 2019-05-17 | 2.0 |
| 502 | Fatal | 2019-05-18 | 1.0 |
| 503 | Fatal | 2019-05-19 | 5.0 |
| 504 | Fatal | 2019-05-20 | 5.0 |
| 505 | Fatal | 2019-05-21 | 0.0 |
| 506 | Fatal | 2019-05-22 | 3.0 |
| 507 | Fatal | 2019-05-23 | 4.0 |
| 508 | Fatal | 2019-05-24 | 5.0 |
| 509 | Fatal | 2019-05-25 | 2.0 |
| 510 | Fatal | 2019-05-26 | 2.0 |
| 511 | Fatal | 2019-05-27 | 1.0 |
| 512 | Fatal | 2019-05-28 | 4.0 |
| 513 | Fatal | 2019-05-29 | 1.0 |
| 514 | Fatal | 2019-05-30 | 7.0 |
| 515 | Fatal | 2019-05-31 | 5.0 |
| 516 | Fatal | 2019-06-01 | 1.0 |
| 517 | Fatal | 2019-06-02 | 4.0 |
| 518 | Fatal | 2019-06-03 | 4.0 |
| 519 | Fatal | 2019-06-04 | 5.0 |
| 520 | Fatal | 2019-06-05 | 4.0 |
| 521 | Fatal | 2019-06-06 | 2.0 |
| 522 | Fatal | 2019-06-07 | 7.0 |
| 523 | Fatal | 2019-06-08 | 1.0 |
| 524 | Fatal | 2019-06-09 | 3.0 |
| 525 | Fatal | 2019-06-10 | 1.0 |
| 526 | Fatal | 2019-06-11 | 1.0 |
| 527 | Fatal | 2019-06-12 | 4.0 |
| 528 | Fatal | 2019-06-13 | 2.0 |
| 529 | Fatal | 2019-06-14 | 1.0 |
| 530 | Fatal | 2019-06-15 | 2.0 |
| 531 | Fatal | 2019-06-16 | 2.0 |
| 532 | Fatal | 2019-06-17 | 6.0 |
| 533 | Fatal | 2019-06-18 | 0.0 |
| 534 | Fatal | 2019-06-19 | 2.0 |
| 535 | Fatal | 2019-06-20 | 4.0 |
| 536 | Fatal | 2019-06-21 | 4.0 |
| 537 | Fatal | 2019-06-22 | 3.0 |
| 538 | Fatal | 2019-06-23 | 1.0 |
| 539 | Fatal | 2019-06-24 | 5.0 |
| 540 | Fatal | 2019-06-25 | 3.0 |
| 541 | Fatal | 2019-06-26 | 8.0 |
| 542 | Fatal | 2019-06-27 | 4.0 |
| 543 | Fatal | 2019-06-28 | 6.0 |
| 544 | Fatal | 2019-06-29 | 9.0 |
| 545 | Fatal | 2019-06-30 | 7.0 |
| 546 | Fatal | 2019-07-01 | 4.0 |
| 547 | Fatal | 2019-07-02 | 2.0 |
| 548 | Fatal | 2019-07-03 | 9.0 |
| 549 | Fatal | 2019-07-04 | 7.0 |
| 550 | Fatal | 2019-07-05 | 3.0 |
| 551 | Fatal | 2019-07-06 | 8.0 |
| 552 | Fatal | 2019-07-07 | 5.0 |
| 553 | Fatal | 2019-07-08 | 2.0 |
| 554 | Fatal | 2019-07-09 | 5.0 |
| 555 | Fatal | 2019-07-10 | 2.0 |
| 556 | Fatal | 2019-07-11 | 3.0 |
| 557 | Fatal | 2019-07-12 | 4.0 |
| 558 | Fatal | 2019-07-13 | 5.0 |
| 559 | Fatal | 2019-07-14 | 7.0 |
| 560 | Fatal | 2019-07-15 | 4.0 |
| 561 | Fatal | 2019-07-16 | 3.0 |
| 562 | Fatal | 2019-07-17 | 3.0 |
| 563 | Fatal | 2019-07-18 | 3.0 |
| 564 | Fatal | 2019-07-19 | 3.0 |
| 565 | Fatal | 2019-07-20 | 0.0 |
| 566 | Fatal | 2019-07-21 | 2.0 |
| 567 | Fatal | 2019-07-22 | 7.0 |
| 568 | Fatal | 2019-07-23 | 6.0 |
| 569 | Fatal | 2019-07-24 | 5.0 |
| 570 | Fatal | 2019-07-25 | 6.0 |
| 571 | Fatal | 2019-07-26 | 2.0 |
| 572 | Fatal | 2019-07-27 | 6.0 |
| 573 | Fatal | 2019-07-28 | 8.0 |
| 574 | Fatal | 2019-07-29 | 8.0 |
| 575 | Fatal | 2019-07-30 | 2.0 |
| 576 | Fatal | 2019-07-31 | 7.0 |
| 577 | Fatal | 2019-08-01 | 2.0 |
| 578 | Fatal | 2019-08-02 | 9.0 |
| 579 | Fatal | 2019-08-03 | 5.0 |
| 580 | Fatal | 2019-08-04 | 4.0 |
| 581 | Fatal | 2019-08-05 | 1.0 |
| 582 | Fatal | 2019-08-06 | 5.0 |
| 583 | Fatal | 2019-08-07 | 6.0 |
| 584 | Fatal | 2019-08-08 | 3.0 |
| 585 | Fatal | 2019-08-09 | 4.0 |
| 586 | Fatal | 2019-08-10 | 4.0 |
| 587 | Fatal | 2019-08-11 | 2.0 |
| 588 | Fatal | 2019-08-12 | 5.0 |
| 589 | Fatal | 2019-08-13 | 3.0 |
| 590 | Fatal | 2019-08-14 | 2.0 |
| 591 | Fatal | 2019-08-15 | 7.0 |
| 592 | Fatal | 2019-08-16 | 8.0 |
| 593 | Fatal | 2019-08-17 | 8.0 |
| 594 | Fatal | 2019-08-18 | 2.0 |
| 595 | Fatal | 2019-08-19 | 3.0 |
| 596 | Fatal | 2019-08-20 | 2.0 |
| 597 | Fatal | 2019-08-21 | 2.0 |
| 598 | Fatal | 2019-08-22 | 4.0 |
| 599 | Fatal | 2019-08-23 | 4.0 |
| 600 | Fatal | 2019-08-24 | 7.0 |
| 601 | Fatal | 2019-08-25 | 4.0 |
| 602 | Fatal | 2019-08-26 | 2.0 |
| 603 | Fatal | 2019-08-27 | 7.0 |
| 604 | Fatal | 2019-08-28 | 3.0 |
| 605 | Fatal | 2019-08-29 | 2.0 |
| 606 | Fatal | 2019-08-30 | 4.0 |
| 607 | Fatal | 2019-08-31 | 4.0 |
| 608 | Fatal | 2019-09-01 | 5.0 |
| 609 | Fatal | 2019-09-02 | 3.0 |
| 610 | Fatal | 2019-09-03 | 3.0 |
| 611 | Fatal | 2019-09-04 | 5.0 |
| 612 | Fatal | 2019-09-05 | 4.0 |
| 613 | Fatal | 2019-09-06 | 1.0 |
| 614 | Fatal | 2019-09-07 | 7.0 |
| 615 | Fatal | 2019-09-08 | 8.0 |
| 616 | Fatal | 2019-09-09 | 4.0 |
| 617 | Fatal | 2019-09-10 | 9.0 |
| 618 | Fatal | 2019-09-11 | 4.0 |
| 619 | Fatal | 2019-09-12 | 5.0 |
| 620 | Fatal | 2019-09-13 | 4.0 |
| 621 | Fatal | 2019-09-14 | 3.0 |
| 622 | Fatal | 2019-09-15 | 3.0 |
| 623 | Fatal | 2019-09-16 | 5.0 |
| 624 | Fatal | 2019-09-17 | 3.0 |
| 625 | Fatal | 2019-09-18 | 5.0 |
| 626 | Fatal | 2019-09-19 | 5.0 |
| 627 | Fatal | 2019-09-20 | 5.0 |
| 628 | Fatal | 2019-09-21 | 5.0 |
| 629 | Fatal | 2019-09-22 | 4.0 |
| 630 | Fatal | 2019-09-23 | 4.0 |
| 631 | Fatal | 2019-09-24 | 6.0 |
| 632 | Fatal | 2019-09-25 | 4.0 |
| 633 | Fatal | 2019-09-26 | 2.0 |
| 634 | Fatal | 2019-09-27 | 1.0 |
| 635 | Fatal | 2019-09-28 | 3.0 |
| 636 | Fatal | 2019-09-29 | 5.0 |
| 637 | Fatal | 2019-09-30 | 4.0 |
| 638 | Fatal | 2019-10-01 | 8.0 |
| 639 | Fatal | 2019-10-02 | 2.0 |
| 640 | Fatal | 2019-10-03 | 3.0 |
| 641 | Fatal | 2019-10-04 | 5.0 |
| 642 | Fatal | 2019-10-05 | 5.0 |
| 643 | Fatal | 2019-10-06 | 3.0 |
| 644 | Fatal | 2019-10-07 | 5.0 |
| 645 | Fatal | 2019-10-08 | 8.0 |
| 646 | Fatal | 2019-10-09 | 4.0 |
| 647 | Fatal | 2019-10-10 | 6.0 |
| 648 | Fatal | 2019-10-11 | 6.0 |
| 649 | Fatal | 2019-10-12 | 5.0 |
| 650 | Fatal | 2019-10-13 | 4.0 |
| 651 | Fatal | 2019-10-14 | 5.0 |
| 652 | Fatal | 2019-10-15 | 3.0 |
| 653 | Fatal | 2019-10-16 | 3.0 |
| 654 | Fatal | 2019-10-17 | 3.0 |
| 655 | Fatal | 2019-10-18 | 3.0 |
| 656 | Fatal | 2019-10-19 | 4.0 |
| 657 | Fatal | 2019-10-20 | 3.0 |
| 658 | Fatal | 2019-10-21 | 2.0 |
| 659 | Fatal | 2019-10-22 | 2.0 |
| 660 | Fatal | 2019-10-23 | 4.0 |
| 661 | Fatal | 2019-10-24 | 3.0 |
| 662 | Fatal | 2019-10-25 | 2.0 |
| 663 | Fatal | 2019-10-26 | 3.0 |
| 664 | Fatal | 2019-10-27 | 3.0 |
| 665 | Fatal | 2019-10-28 | 6.0 |
| 666 | Fatal | 2019-10-29 | 3.0 |
| 667 | Fatal | 2019-10-30 | 2.0 |
| 668 | Fatal | 2019-10-31 | 4.0 |
| 669 | Fatal | 2019-11-01 | 9.0 |
| 670 | Fatal | 2019-11-02 | 7.0 |
| 671 | Fatal | 2019-11-03 | 9.0 |
| 672 | Fatal | 2019-11-04 | 4.0 |
| 673 | Fatal | 2019-11-05 | 3.0 |
| 674 | Fatal | 2019-11-06 | 0.0 |
| 675 | Fatal | 2019-11-07 | 3.0 |
| 676 | Fatal | 2019-11-08 | 2.0 |
| 677 | Fatal | 2019-11-09 | 5.0 |
| 678 | Fatal | 2019-11-10 | 4.0 |
| 679 | Fatal | 2019-11-11 | 4.0 |
| 680 | Fatal | 2019-11-12 | 4.0 |
| 681 | Fatal | 2019-11-13 | 0.0 |
| 682 | Fatal | 2019-11-14 | 4.0 |
| 683 | Fatal | 2019-11-15 | 2.0 |
| 684 | Fatal | 2019-11-16 | 6.0 |
| 685 | Fatal | 2019-11-17 | 3.0 |
| 686 | Fatal | 2019-11-18 | 2.0 |
| 687 | Fatal | 2019-11-19 | 4.0 |
| 688 | Fatal | 2019-11-20 | 6.0 |
| 689 | Fatal | 2019-11-21 | 5.0 |
| 690 | Fatal | 2019-11-22 | 2.0 |
| 691 | Fatal | 2019-11-23 | 4.0 |
| 692 | Fatal | 2019-11-24 | 3.0 |
| 693 | Fatal | 2019-11-25 | 2.0 |
| 694 | Fatal | 2019-11-26 | 6.0 |
| 695 | Fatal | 2019-11-27 | 3.0 |
| 696 | Fatal | 2019-11-28 | 3.0 |
| 697 | Fatal | 2019-11-29 | 8.0 |
| 698 | Fatal | 2019-11-30 | 7.0 |
| 699 | Fatal | 2019-12-01 | 6.0 |
| 700 | Fatal | 2019-12-02 | 7.0 |
| 701 | Fatal | 2019-12-03 | 3.0 |
| 702 | Fatal | 2019-12-04 | 2.0 |
| 703 | Fatal | 2019-12-05 | 5.0 |
| 704 | Fatal | 2019-12-06 | 5.0 |
| 705 | Fatal | 2019-12-07 | 3.0 |
| 706 | Fatal | 2019-12-08 | 3.0 |
| 707 | Fatal | 2019-12-09 | 4.0 |
| 708 | Fatal | 2019-12-10 | 6.0 |
| 709 | Fatal | 2019-12-11 | 10.0 |
| 710 | Fatal | 2019-12-12 | 6.0 |
| 711 | Fatal | 2019-12-13 | 4.0 |
| 712 | Fatal | 2019-12-14 | 1.0 |
| 713 | Fatal | 2019-12-15 | 7.0 |
| 714 | Fatal | 2019-12-16 | 8.0 |
| 715 | Fatal | 2019-12-17 | 3.0 |
| 716 | Fatal | 2019-12-18 | 3.0 |
| 717 | Fatal | 2019-12-19 | 3.0 |
| 718 | Fatal | 2019-12-20 | 6.0 |
| 719 | Fatal | 2019-12-21 | 8.0 |
| 720 | Fatal | 2019-12-22 | 4.0 |
| 721 | Fatal | 2019-12-23 | 4.0 |
| 722 | Fatal | 2019-12-24 | 3.0 |
| 723 | Fatal | 2019-12-25 | 6.0 |
| 724 | Fatal | 2019-12-26 | 3.0 |
| 725 | Fatal | 2019-12-27 | 1.0 |
| 726 | Fatal | 2019-12-28 | 2.0 |
| 727 | Fatal | 2019-12-29 | 2.0 |
| 728 | Fatal | 2019-12-30 | 2.0 |
| 729 | Fatal | 2019-12-31 | 9.0 |
| 730 | Serious | 2018-01-01 | 59.0 |
| 731 | Serious | 2018-01-02 | 35.0 |
| 732 | Serious | 2018-01-03 | 41.0 |
| 733 | Serious | 2018-01-04 | 48.0 |
| 734 | Serious | 2018-01-05 | 44.0 |
| 735 | Serious | 2018-01-06 | 34.0 |
| 736 | Serious | 2018-01-07 | 37.0 |
| 737 | Serious | 2018-01-08 | 56.0 |
| 738 | Serious | 2018-01-09 | 44.0 |
| 739 | Serious | 2018-01-10 | 74.0 |
| 740 | Serious | 2018-01-11 | 60.0 |
| 741 | Serious | 2018-01-12 | 68.0 |
| 742 | Serious | 2018-01-13 | 47.0 |
| 743 | Serious | 2018-01-14 | 41.0 |
| 744 | Serious | 2018-01-15 | 55.0 |
| 745 | Serious | 2018-01-16 | 77.0 |
| 746 | Serious | 2018-01-17 | 47.0 |
| 747 | Serious | 2018-01-18 | 66.0 |
| 748 | Serious | 2018-01-19 | 78.0 |
| 749 | Serious | 2018-01-20 | 37.0 |
| 750 | Serious | 2018-01-21 | 32.0 |
| 751 | Serious | 2018-01-22 | 46.0 |
| 752 | Serious | 2018-01-23 | 52.0 |
| 753 | Serious | 2018-01-24 | 61.0 |
| 754 | Serious | 2018-01-25 | 68.0 |
| 755 | Serious | 2018-01-26 | 76.0 |
| 756 | Serious | 2018-01-27 | 50.0 |
| 757 | Serious | 2018-01-28 | 40.0 |
| 758 | Serious | 2018-01-29 | 59.0 |
| 759 | Serious | 2018-01-30 | 67.0 |
| 760 | Serious | 2018-01-31 | 42.0 |
| 761 | Serious | 2018-02-01 | 72.0 |
| 762 | Serious | 2018-02-02 | 65.0 |
| 763 | Serious | 2018-02-03 | 59.0 |
| 764 | Serious | 2018-02-04 | 40.0 |
| 765 | Serious | 2018-02-05 | 49.0 |
| 766 | Serious | 2018-02-06 | 41.0 |
| 767 | Serious | 2018-02-07 | 63.0 |
| 768 | Serious | 2018-02-08 | 45.0 |
| 769 | Serious | 2018-02-09 | 69.0 |
| 770 | Serious | 2018-02-10 | 39.0 |
| 771 | Serious | 2018-02-11 | 42.0 |
| 772 | Serious | 2018-02-12 | 76.0 |
| 773 | Serious | 2018-02-13 | 70.0 |
| 774 | Serious | 2018-02-14 | 64.0 |
| 775 | Serious | 2018-02-15 | 53.0 |
| 776 | Serious | 2018-02-16 | 67.0 |
| 777 | Serious | 2018-02-17 | 56.0 |
| 778 | Serious | 2018-02-18 | 38.0 |
| 779 | Serious | 2018-02-19 | 51.0 |
| 780 | Serious | 2018-02-20 | 51.0 |
| 781 | Serious | 2018-02-21 | 60.0 |
| 782 | Serious | 2018-02-22 | 50.0 |
| 783 | Serious | 2018-02-23 | 71.0 |
| 784 | Serious | 2018-02-24 | 58.0 |
| 785 | Serious | 2018-02-25 | 45.0 |
| 786 | Serious | 2018-02-26 | 46.0 |
| 787 | Serious | 2018-02-27 | 69.0 |
| 788 | Serious | 2018-02-28 | 35.0 |
| 789 | Serious | 2018-03-01 | 31.0 |
| 790 | Serious | 2018-03-02 | 23.0 |
| 791 | Serious | 2018-03-03 | 26.0 |
| 792 | Serious | 2018-03-04 | 39.0 |
| 793 | Serious | 2018-03-05 | 55.0 |
| 794 | Serious | 2018-03-06 | 35.0 |
| 795 | Serious | 2018-03-07 | 36.0 |
| 796 | Serious | 2018-03-08 | 60.0 |
| 797 | Serious | 2018-03-09 | 68.0 |
| 798 | Serious | 2018-03-10 | 56.0 |
| 799 | Serious | 2018-03-11 | 44.0 |
| 800 | Serious | 2018-03-12 | 45.0 |
| 801 | Serious | 2018-03-13 | 53.0 |
| 802 | Serious | 2018-03-14 | 42.0 |
| 803 | Serious | 2018-03-15 | 58.0 |
| 804 | Serious | 2018-03-16 | 56.0 |
| 805 | Serious | 2018-03-17 | 49.0 |
| 806 | Serious | 2018-03-18 | 25.0 |
| 807 | Serious | 2018-03-19 | 37.0 |
| 808 | Serious | 2018-03-20 | 58.0 |
| 809 | Serious | 2018-03-21 | 64.0 |
| 810 | Serious | 2018-03-22 | 46.0 |
| 811 | Serious | 2018-03-23 | 55.0 |
| 812 | Serious | 2018-03-24 | 48.0 |
| 813 | Serious | 2018-03-25 | 52.0 |
| 814 | Serious | 2018-03-26 | 57.0 |
| 815 | Serious | 2018-03-27 | 49.0 |
| 816 | Serious | 2018-03-28 | 48.0 |
| 817 | Serious | 2018-03-29 | 57.0 |
| 818 | Serious | 2018-03-30 | 45.0 |
| 819 | Serious | 2018-03-31 | 49.0 |
| 820 | Serious | 2018-04-01 | 46.0 |
| 821 | Serious | 2018-04-02 | 33.0 |
| 822 | Serious | 2018-04-03 | 40.0 |
| 823 | Serious | 2018-04-04 | 37.0 |
| 824 | Serious | 2018-04-05 | 67.0 |
| 825 | Serious | 2018-04-06 | 40.0 |
| 826 | Serious | 2018-04-07 | 41.0 |
| 827 | Serious | 2018-04-08 | 40.0 |
| 828 | Serious | 2018-04-09 | 43.0 |
| 829 | Serious | 2018-04-10 | 38.0 |
| 830 | Serious | 2018-04-11 | 50.0 |
| 831 | Serious | 2018-04-12 | 39.0 |
| 832 | Serious | 2018-04-13 | 61.0 |
| 833 | Serious | 2018-04-14 | 68.0 |
| 834 | Serious | 2018-04-15 | 56.0 |
| 835 | Serious | 2018-04-16 | 49.0 |
| 836 | Serious | 2018-04-17 | 50.0 |
| 837 | Serious | 2018-04-18 | 96.0 |
| 838 | Serious | 2018-04-19 | 83.0 |
| 839 | Serious | 2018-04-20 | 95.0 |
| 840 | Serious | 2018-04-21 | 82.0 |
| 841 | Serious | 2018-04-22 | 57.0 |
| 842 | Serious | 2018-04-23 | 50.0 |
| 843 | Serious | 2018-04-24 | 63.0 |
| 844 | Serious | 2018-04-25 | 60.0 |
| 845 | Serious | 2018-04-26 | 68.0 |
| 846 | Serious | 2018-04-27 | 62.0 |
| 847 | Serious | 2018-04-28 | 59.0 |
| 848 | Serious | 2018-04-29 | 37.0 |
| 849 | Serious | 2018-04-30 | 51.0 |
| 850 | Serious | 2018-05-01 | 52.0 |
| 851 | Serious | 2018-05-02 | 56.0 |
| 852 | Serious | 2018-05-03 | 62.0 |
| 853 | Serious | 2018-05-04 | 83.0 |
| 854 | Serious | 2018-05-05 | 72.0 |
| 855 | Serious | 2018-05-06 | 88.0 |
| 856 | Serious | 2018-05-07 | 84.0 |
| 857 | Serious | 2018-05-08 | 78.0 |
| 858 | Serious | 2018-05-09 | 77.0 |
| 859 | Serious | 2018-05-10 | 67.0 |
| 860 | Serious | 2018-05-11 | 53.0 |
| 861 | Serious | 2018-05-12 | 60.0 |
| 862 | Serious | 2018-05-13 | 47.0 |
| 863 | Serious | 2018-05-14 | 60.0 |
| 864 | Serious | 2018-05-15 | 81.0 |
| 865 | Serious | 2018-05-16 | 60.0 |
| 866 | Serious | 2018-05-17 | 73.0 |
| 867 | Serious | 2018-05-18 | 62.0 |
| 868 | Serious | 2018-05-19 | 65.0 |
| 869 | Serious | 2018-05-20 | 64.0 |
| 870 | Serious | 2018-05-21 | 60.0 |
| 871 | Serious | 2018-05-22 | 72.0 |
| 872 | Serious | 2018-05-23 | 80.0 |
| 873 | Serious | 2018-05-24 | 64.0 |
| 874 | Serious | 2018-05-25 | 73.0 |
| 875 | Serious | 2018-05-26 | 57.0 |
| 876 | Serious | 2018-05-27 | 58.0 |
| 877 | Serious | 2018-05-28 | 60.0 |
| 878 | Serious | 2018-05-29 | 49.0 |
| 879 | Serious | 2018-05-30 | 50.0 |
| 880 | Serious | 2018-05-31 | 43.0 |
| 881 | Serious | 2018-06-01 | 58.0 |
| 882 | Serious | 2018-06-02 | 56.0 |
| 883 | Serious | 2018-06-03 | 68.0 |
| 884 | Serious | 2018-06-04 | 52.0 |
| 885 | Serious | 2018-06-05 | 58.0 |
| 886 | Serious | 2018-06-06 | 63.0 |
| 887 | Serious | 2018-06-07 | 62.0 |
| 888 | Serious | 2018-06-08 | 63.0 |
| 889 | Serious | 2018-06-09 | 63.0 |
| 890 | Serious | 2018-06-10 | 63.0 |
| 891 | Serious | 2018-06-11 | 77.0 |
| 892 | Serious | 2018-06-12 | 62.0 |
| 893 | Serious | 2018-06-13 | 61.0 |
| 894 | Serious | 2018-06-14 | 69.0 |
| 895 | Serious | 2018-06-15 | 73.0 |
| 896 | Serious | 2018-06-16 | 67.0 |
| 897 | Serious | 2018-06-17 | 54.0 |
| 898 | Serious | 2018-06-18 | 63.0 |
| 899 | Serious | 2018-06-19 | 70.0 |
| 900 | Serious | 2018-06-20 | 56.0 |
| 901 | Serious | 2018-06-21 | 67.0 |
| 902 | Serious | 2018-06-22 | 86.0 |
| 903 | Serious | 2018-06-23 | 77.0 |
| 904 | Serious | 2018-06-24 | 82.0 |
| 905 | Serious | 2018-06-25 | 69.0 |
| 906 | Serious | 2018-06-26 | 87.0 |
| 907 | Serious | 2018-06-27 | 92.0 |
| 908 | Serious | 2018-06-28 | 80.0 |
| 909 | Serious | 2018-06-29 | 75.0 |
| 910 | Serious | 2018-06-30 | 78.0 |
| 911 | Serious | 2018-07-01 | 79.0 |
| 912 | Serious | 2018-07-02 | 67.0 |
| 913 | Serious | 2018-07-03 | 59.0 |
| 914 | Serious | 2018-07-04 | 63.0 |
| 915 | Serious | 2018-07-05 | 82.0 |
| 916 | Serious | 2018-07-06 | 74.0 |
| 917 | Serious | 2018-07-07 | 83.0 |
| 918 | Serious | 2018-07-08 | 70.0 |
| 919 | Serious | 2018-07-09 | 56.0 |
| 920 | Serious | 2018-07-10 | 72.0 |
| 921 | Serious | 2018-07-11 | 63.0 |
| 922 | Serious | 2018-07-12 | 64.0 |
| 923 | Serious | 2018-07-13 | 80.0 |
| 924 | Serious | 2018-07-14 | 56.0 |
| 925 | Serious | 2018-07-15 | 67.0 |
| 926 | Serious | 2018-07-16 | 77.0 |
| 927 | Serious | 2018-07-17 | 67.0 |
| 928 | Serious | 2018-07-18 | 59.0 |
| 929 | Serious | 2018-07-19 | 62.0 |
| 930 | Serious | 2018-07-20 | 54.0 |
| 931 | Serious | 2018-07-21 | 58.0 |
| 932 | Serious | 2018-07-22 | 52.0 |
| 933 | Serious | 2018-07-23 | 68.0 |
| 934 | Serious | 2018-07-24 | 70.0 |
| 935 | Serious | 2018-07-25 | 77.0 |
| 936 | Serious | 2018-07-26 | 62.0 |
| 937 | Serious | 2018-07-27 | 58.0 |
| 938 | Serious | 2018-07-28 | 58.0 |
| 939 | Serious | 2018-07-29 | 60.0 |
| 940 | Serious | 2018-07-30 | 66.0 |
| 941 | Serious | 2018-07-31 | 66.0 |
| 942 | Serious | 2018-08-01 | 52.0 |
| 943 | Serious | 2018-08-02 | 55.0 |
| 944 | Serious | 2018-08-03 | 80.0 |
| 945 | Serious | 2018-08-04 | 69.0 |
| 946 | Serious | 2018-08-05 | 75.0 |
| 947 | Serious | 2018-08-06 | 64.0 |
| 948 | Serious | 2018-08-07 | 66.0 |
| 949 | Serious | 2018-08-08 | 55.0 |
| 950 | Serious | 2018-08-09 | 66.0 |
| 951 | Serious | 2018-08-10 | 58.0 |
| 952 | Serious | 2018-08-11 | 64.0 |
| 953 | Serious | 2018-08-12 | 47.0 |
| 954 | Serious | 2018-08-13 | 46.0 |
| 955 | Serious | 2018-08-14 | 48.0 |
| 956 | Serious | 2018-08-15 | 58.0 |
| 957 | Serious | 2018-08-16 | 53.0 |
| 958 | Serious | 2018-08-17 | 67.0 |
| 959 | Serious | 2018-08-18 | 49.0 |
| 960 | Serious | 2018-08-19 | 45.0 |
| 961 | Serious | 2018-08-20 | 57.0 |
| 962 | Serious | 2018-08-21 | 53.0 |
| 963 | Serious | 2018-08-22 | 58.0 |
| 964 | Serious | 2018-08-23 | 61.0 |
| 965 | Serious | 2018-08-24 | 71.0 |
| 966 | Serious | 2018-08-25 | 58.0 |
| 967 | Serious | 2018-08-26 | 54.0 |
| 968 | Serious | 2018-08-27 | 42.0 |
| 969 | Serious | 2018-08-28 | 48.0 |
| 970 | Serious | 2018-08-29 | 55.0 |
| 971 | Serious | 2018-08-30 | 44.0 |
| 972 | Serious | 2018-08-31 | 77.0 |
| 973 | Serious | 2018-09-01 | 64.0 |
| 974 | Serious | 2018-09-02 | 66.0 |
| 975 | Serious | 2018-09-03 | 66.0 |
| 976 | Serious | 2018-09-04 | 55.0 |
| 977 | Serious | 2018-09-05 | 73.0 |
| 978 | Serious | 2018-09-06 | 76.0 |
| 979 | Serious | 2018-09-07 | 54.0 |
| 980 | Serious | 2018-09-08 | 52.0 |
| 981 | Serious | 2018-09-09 | 57.0 |
| 982 | Serious | 2018-09-10 | 48.0 |
| 983 | Serious | 2018-09-11 | 65.0 |
| 984 | Serious | 2018-09-12 | 62.0 |
| 985 | Serious | 2018-09-13 | 67.0 |
| 986 | Serious | 2018-09-14 | 60.0 |
| 987 | Serious | 2018-09-15 | 54.0 |
| 988 | Serious | 2018-09-16 | 64.0 |
| 989 | Serious | 2018-09-17 | 63.0 |
| 990 | Serious | 2018-09-18 | 72.0 |
| 991 | Serious | 2018-09-19 | 65.0 |
| 992 | Serious | 2018-09-20 | 58.0 |
| 993 | Serious | 2018-09-21 | 64.0 |
| 994 | Serious | 2018-09-22 | 64.0 |
| 995 | Serious | 2018-09-23 | 43.0 |
| 996 | Serious | 2018-09-24 | 77.0 |
| 997 | Serious | 2018-09-25 | 67.0 |
| 998 | Serious | 2018-09-26 | 77.0 |
| 999 | Serious | 2018-09-27 | 75.0 |
| 1000 | Serious | 2018-09-28 | 83.0 |
| 1001 | Serious | 2018-09-29 | 68.0 |
| 1002 | Serious | 2018-09-30 | 54.0 |
| 1003 | Serious | 2018-10-01 | 64.0 |
| 1004 | Serious | 2018-10-02 | 53.0 |
| 1005 | Serious | 2018-10-03 | 62.0 |
| 1006 | Serious | 2018-10-04 | 60.0 |
| 1007 | Serious | 2018-10-05 | 70.0 |
| 1008 | Serious | 2018-10-06 | 75.0 |
| 1009 | Serious | 2018-10-07 | 48.0 |
| 1010 | Serious | 2018-10-08 | 54.0 |
| 1011 | Serious | 2018-10-09 | 74.0 |
| 1012 | Serious | 2018-10-10 | 97.0 |
| 1013 | Serious | 2018-10-11 | 67.0 |
| 1014 | Serious | 2018-10-12 | 76.0 |
| 1015 | Serious | 2018-10-13 | 59.0 |
| 1016 | Serious | 2018-10-14 | 54.0 |
| 1017 | Serious | 2018-10-15 | 61.0 |
| 1018 | Serious | 2018-10-16 | 60.0 |
| 1019 | Serious | 2018-10-17 | 56.0 |
| 1020 | Serious | 2018-10-18 | 66.0 |
| 1021 | Serious | 2018-10-19 | 75.0 |
| 1022 | Serious | 2018-10-20 | 80.0 |
| 1023 | Serious | 2018-10-21 | 60.0 |
| 1024 | Serious | 2018-10-22 | 56.0 |
| 1025 | Serious | 2018-10-23 | 49.0 |
| 1026 | Serious | 2018-10-24 | 71.0 |
| 1027 | Serious | 2018-10-25 | 57.0 |
| 1028 | Serious | 2018-10-26 | 72.0 |
| 1029 | Serious | 2018-10-27 | 56.0 |
| 1030 | Serious | 2018-10-28 | 57.0 |
| 1031 | Serious | 2018-10-29 | 61.0 |
| 1032 | Serious | 2018-10-30 | 50.0 |
| 1033 | Serious | 2018-10-31 | 80.0 |
| 1034 | Serious | 2018-11-01 | 66.0 |
| 1035 | Serious | 2018-11-02 | 75.0 |
| 1036 | Serious | 2018-11-03 | 63.0 |
| 1037 | Serious | 2018-11-04 | 33.0 |
| 1038 | Serious | 2018-11-05 | 64.0 |
| 1039 | Serious | 2018-11-06 | 59.0 |
| 1040 | Serious | 2018-11-07 | 62.0 |
| 1041 | Serious | 2018-11-08 | 74.0 |
| 1042 | Serious | 2018-11-09 | 66.0 |
| 1043 | Serious | 2018-11-10 | 56.0 |
| 1044 | Serious | 2018-11-11 | 54.0 |
| 1045 | Serious | 2018-11-12 | 61.0 |
| 1046 | Serious | 2018-11-13 | 72.0 |
| 1047 | Serious | 2018-11-14 | 60.0 |
| 1048 | Serious | 2018-11-15 | 71.0 |
| 1049 | Serious | 2018-11-16 | 61.0 |
| 1050 | Serious | 2018-11-17 | 66.0 |
| 1051 | Serious | 2018-11-18 | 61.0 |
| 1052 | Serious | 2018-11-19 | 54.0 |
| 1053 | Serious | 2018-11-20 | 58.0 |
| 1054 | Serious | 2018-11-21 | 78.0 |
| 1055 | Serious | 2018-11-22 | 68.0 |
| 1056 | Serious | 2018-11-23 | 69.0 |
| 1057 | Serious | 2018-11-24 | 50.0 |
| 1058 | Serious | 2018-11-25 | 50.0 |
| 1059 | Serious | 2018-11-26 | 66.0 |
| 1060 | Serious | 2018-11-27 | 77.0 |
| 1061 | Serious | 2018-11-28 | 62.0 |
| 1062 | Serious | 2018-11-29 | 76.0 |
| 1063 | Serious | 2018-11-30 | 79.0 |
| 1064 | Serious | 2018-12-01 | 49.0 |
| 1065 | Serious | 2018-12-02 | 47.0 |
| 1066 | Serious | 2018-12-03 | 53.0 |
| 1067 | Serious | 2018-12-04 | 77.0 |
| 1068 | Serious | 2018-12-05 | 84.0 |
| 1069 | Serious | 2018-12-06 | 76.0 |
| 1070 | Serious | 2018-12-07 | 79.0 |
| 1071 | Serious | 2018-12-08 | 43.0 |
| 1072 | Serious | 2018-12-09 | 57.0 |
| 1073 | Serious | 2018-12-10 | 56.0 |
| 1074 | Serious | 2018-12-11 | 81.0 |
| 1075 | Serious | 2018-12-12 | 68.0 |
| 1076 | Serious | 2018-12-13 | 77.0 |
| 1077 | Serious | 2018-12-14 | 77.0 |
| 1078 | Serious | 2018-12-15 | 53.0 |
| 1079 | Serious | 2018-12-16 | 49.0 |
| 1080 | Serious | 2018-12-17 | 56.0 |
| 1081 | Serious | 2018-12-18 | 81.0 |
| 1082 | Serious | 2018-12-19 | 52.0 |
| 1083 | Serious | 2018-12-20 | 56.0 |
| 1084 | Serious | 2018-12-21 | 56.0 |
| 1085 | Serious | 2018-12-22 | 63.0 |
| 1086 | Serious | 2018-12-23 | 52.0 |
| 1087 | Serious | 2018-12-24 | 55.0 |
| 1088 | Serious | 2018-12-25 | 31.0 |
| 1089 | Serious | 2018-12-26 | 35.0 |
| 1090 | Serious | 2018-12-27 | 41.0 |
| 1091 | Serious | 2018-12-28 | 51.0 |
| 1092 | Serious | 2018-12-29 | 37.0 |
| 1093 | Serious | 2018-12-30 | 24.0 |
| 1094 | Serious | 2018-12-31 | 47.0 |
| 1095 | Serious | 2019-01-01 | 54.0 |
| 1096 | Serious | 2019-01-02 | 27.0 |
| 1097 | Serious | 2019-01-03 | 46.0 |
| 1098 | Serious | 2019-01-04 | 40.0 |
| 1099 | Serious | 2019-01-05 | 37.0 |
| 1100 | Serious | 2019-01-06 | 37.0 |
| 1101 | Serious | 2019-01-07 | 47.0 |
| 1102 | Serious | 2019-01-08 | 63.0 |
| 1103 | Serious | 2019-01-09 | 59.0 |
| 1104 | Serious | 2019-01-10 | 70.0 |
| 1105 | Serious | 2019-01-11 | 62.0 |
| 1106 | Serious | 2019-01-12 | 49.0 |
| 1107 | Serious | 2019-01-13 | 35.0 |
| 1108 | Serious | 2019-01-14 | 52.0 |
| 1109 | Serious | 2019-01-15 | 50.0 |
| 1110 | Serious | 2019-01-16 | 57.0 |
| 1111 | Serious | 2019-01-17 | 66.0 |
| 1112 | Serious | 2019-01-18 | 65.0 |
| 1113 | Serious | 2019-01-19 | 40.0 |
| 1114 | Serious | 2019-01-20 | 42.0 |
| 1115 | Serious | 2019-01-21 | 54.0 |
| 1116 | Serious | 2019-01-22 | 66.0 |
| 1117 | Serious | 2019-01-23 | 58.0 |
| 1118 | Serious | 2019-01-24 | 45.0 |
| 1119 | Serious | 2019-01-25 | 61.0 |
| 1120 | Serious | 2019-01-26 | 51.0 |
| 1121 | Serious | 2019-01-27 | 36.0 |
| 1122 | Serious | 2019-01-28 | 71.0 |
| 1123 | Serious | 2019-01-29 | 56.0 |
| 1124 | Serious | 2019-01-30 | 67.0 |
| 1125 | Serious | 2019-01-31 | 38.0 |
| 1126 | Serious | 2019-02-01 | 41.0 |
| 1127 | Serious | 2019-02-02 | 50.0 |
| 1128 | Serious | 2019-02-03 | 51.0 |
| 1129 | Serious | 2019-02-04 | 41.0 |
| 1130 | Serious | 2019-02-05 | 50.0 |
| 1131 | Serious | 2019-02-06 | 60.0 |
| 1132 | Serious | 2019-02-07 | 68.0 |
| 1133 | Serious | 2019-02-08 | 58.0 |
| 1134 | Serious | 2019-02-09 | 46.0 |
| 1135 | Serious | 2019-02-10 | 38.0 |
| 1136 | Serious | 2019-02-11 | 57.0 |
| 1137 | Serious | 2019-02-12 | 60.0 |
| 1138 | Serious | 2019-02-13 | 63.0 |
| 1139 | Serious | 2019-02-14 | 64.0 |
| 1140 | Serious | 2019-02-15 | 69.0 |
| 1141 | Serious | 2019-02-16 | 46.0 |
| 1142 | Serious | 2019-02-17 | 49.0 |
| 1143 | Serious | 2019-02-18 | 47.0 |
| 1144 | Serious | 2019-02-19 | 52.0 |
| 1145 | Serious | 2019-02-20 | 49.0 |
| 1146 | Serious | 2019-02-21 | 67.0 |
| 1147 | Serious | 2019-02-22 | 59.0 |
| 1148 | Serious | 2019-02-23 | 68.0 |
| 1149 | Serious | 2019-02-24 | 61.0 |
| 1150 | Serious | 2019-02-25 | 50.0 |
| 1151 | Serious | 2019-02-26 | 69.0 |
| 1152 | Serious | 2019-02-27 | 77.0 |
| 1153 | Serious | 2019-02-28 | 51.0 |
| 1154 | Serious | 2019-03-01 | 53.0 |
| 1155 | Serious | 2019-03-02 | 52.0 |
| 1156 | Serious | 2019-03-03 | 37.0 |
| 1157 | Serious | 2019-03-04 | 58.0 |
| 1158 | Serious | 2019-03-05 | 61.0 |
| 1159 | Serious | 2019-03-06 | 67.0 |
| 1160 | Serious | 2019-03-07 | 66.0 |
| 1161 | Serious | 2019-03-08 | 54.0 |
| 1162 | Serious | 2019-03-09 | 59.0 |
| 1163 | Serious | 2019-03-10 | 33.0 |
| 1164 | Serious | 2019-03-11 | 39.0 |
| 1165 | Serious | 2019-03-12 | 51.0 |
| 1166 | Serious | 2019-03-13 | 46.0 |
| 1167 | Serious | 2019-03-14 | 54.0 |
| 1168 | Serious | 2019-03-15 | 59.0 |
| 1169 | Serious | 2019-03-16 | 37.0 |
| 1170 | Serious | 2019-03-17 | 54.0 |
| 1171 | Serious | 2019-03-18 | 52.0 |
| 1172 | Serious | 2019-03-19 | 53.0 |
| 1173 | Serious | 2019-03-20 | 69.0 |
| 1174 | Serious | 2019-03-21 | 63.0 |
| 1175 | Serious | 2019-03-22 | 51.0 |
| 1176 | Serious | 2019-03-23 | 54.0 |
| 1177 | Serious | 2019-03-24 | 64.0 |
| 1178 | Serious | 2019-03-25 | 49.0 |
| 1179 | Serious | 2019-03-26 | 57.0 |
| 1180 | Serious | 2019-03-27 | 56.0 |
| 1181 | Serious | 2019-03-28 | 61.0 |
| 1182 | Serious | 2019-03-29 | 87.0 |
| 1183 | Serious | 2019-03-30 | 76.0 |
| 1184 | Serious | 2019-03-31 | 48.0 |
| 1185 | Serious | 2019-04-01 | 57.0 |
| 1186 | Serious | 2019-04-02 | 49.0 |
| 1187 | Serious | 2019-04-03 | 69.0 |
| 1188 | Serious | 2019-04-04 | 50.0 |
| 1189 | Serious | 2019-04-05 | 62.0 |
| 1190 | Serious | 2019-04-06 | 50.0 |
| 1191 | Serious | 2019-04-07 | 55.0 |
| 1192 | Serious | 2019-04-08 | 44.0 |
| 1193 | Serious | 2019-04-09 | 52.0 |
| 1194 | Serious | 2019-04-10 | 60.0 |
| 1195 | Serious | 2019-04-11 | 61.0 |
| 1196 | Serious | 2019-04-12 | 52.0 |
| 1197 | Serious | 2019-04-13 | 69.0 |
| 1198 | Serious | 2019-04-14 | 36.0 |
| 1199 | Serious | 2019-04-15 | 40.0 |
| 1200 | Serious | 2019-04-16 | 46.0 |
| 1201 | Serious | 2019-04-17 | 50.0 |
| 1202 | Serious | 2019-04-18 | 71.0 |
| 1203 | Serious | 2019-04-19 | 74.0 |
| 1204 | Serious | 2019-04-20 | 87.0 |
| 1205 | Serious | 2019-04-21 | 75.0 |
| 1206 | Serious | 2019-04-22 | 49.0 |
| 1207 | Serious | 2019-04-23 | 53.0 |
| 1208 | Serious | 2019-04-24 | 63.0 |
| 1209 | Serious | 2019-04-25 | 52.0 |
| 1210 | Serious | 2019-04-26 | 66.0 |
| 1211 | Serious | 2019-04-27 | 54.0 |
| 1212 | Serious | 2019-04-28 | 41.0 |
| 1213 | Serious | 2019-04-29 | 57.0 |
| 1214 | Serious | 2019-04-30 | 63.0 |
| 1215 | Serious | 2019-05-01 | 48.0 |
| 1216 | Serious | 2019-05-02 | 72.0 |
| 1217 | Serious | 2019-05-03 | 73.0 |
| 1218 | Serious | 2019-05-04 | 58.0 |
| 1219 | Serious | 2019-05-05 | 57.0 |
| 1220 | Serious | 2019-05-06 | 46.0 |
| 1221 | Serious | 2019-05-07 | 53.0 |
| 1222 | Serious | 2019-05-08 | 62.0 |
| 1223 | Serious | 2019-05-09 | 38.0 |
| 1224 | Serious | 2019-05-10 | 57.0 |
| 1225 | Serious | 2019-05-11 | 55.0 |
| 1226 | Serious | 2019-05-12 | 71.0 |
| 1227 | Serious | 2019-05-13 | 60.0 |
| 1228 | Serious | 2019-05-14 | 70.0 |
| 1229 | Serious | 2019-05-15 | 73.0 |
| 1230 | Serious | 2019-05-16 | 72.0 |
| 1231 | Serious | 2019-05-17 | 46.0 |
| 1232 | Serious | 2019-05-18 | 61.0 |
| 1233 | Serious | 2019-05-19 | 51.0 |
| 1234 | Serious | 2019-05-20 | 59.0 |
| 1235 | Serious | 2019-05-21 | 67.0 |
| 1236 | Serious | 2019-05-22 | 75.0 |
| 1237 | Serious | 2019-05-23 | 68.0 |
| 1238 | Serious | 2019-05-24 | 80.0 |
| 1239 | Serious | 2019-05-25 | 71.0 |
| 1240 | Serious | 2019-05-26 | 41.0 |
| 1241 | Serious | 2019-05-27 | 56.0 |
| 1242 | Serious | 2019-05-28 | 43.0 |
| 1243 | Serious | 2019-05-29 | 56.0 |
| 1244 | Serious | 2019-05-30 | 62.0 |
| 1245 | Serious | 2019-05-31 | 64.0 |
| 1246 | Serious | 2019-06-01 | 76.0 |
| 1247 | Serious | 2019-06-02 | 50.0 |
| 1248 | Serious | 2019-06-03 | 49.0 |
| 1249 | Serious | 2019-06-04 | 48.0 |
| 1250 | Serious | 2019-06-05 | 63.0 |
| 1251 | Serious | 2019-06-06 | 69.0 |
| 1252 | Serious | 2019-06-07 | 69.0 |
| 1253 | Serious | 2019-06-08 | 67.0 |
| 1254 | Serious | 2019-06-09 | 65.0 |
| 1255 | Serious | 2019-06-10 | 77.0 |
| 1256 | Serious | 2019-06-11 | 59.0 |
| 1257 | Serious | 2019-06-12 | 54.0 |
| 1258 | Serious | 2019-06-13 | 51.0 |
| 1259 | Serious | 2019-06-14 | 55.0 |
| 1260 | Serious | 2019-06-15 | 60.0 |
| 1261 | Serious | 2019-06-16 | 58.0 |
| 1262 | Serious | 2019-06-17 | 61.0 |
| 1263 | Serious | 2019-06-18 | 51.0 |
| 1264 | Serious | 2019-06-19 | 55.0 |
| 1265 | Serious | 2019-06-20 | 84.0 |
| 1266 | Serious | 2019-06-21 | 79.0 |
| 1267 | Serious | 2019-06-22 | 85.0 |
| 1268 | Serious | 2019-06-23 | 64.0 |
| 1269 | Serious | 2019-06-24 | 63.0 |
| 1270 | Serious | 2019-06-25 | 53.0 |
| 1271 | Serious | 2019-06-26 | 48.0 |
| 1272 | Serious | 2019-06-27 | 75.0 |
| 1273 | Serious | 2019-06-28 | 89.0 |
| 1274 | Serious | 2019-06-29 | 67.0 |
| 1275 | Serious | 2019-06-30 | 58.0 |
| 1276 | Serious | 2019-07-01 | 65.0 |
| 1277 | Serious | 2019-07-02 | 66.0 |
| 1278 | Serious | 2019-07-03 | 63.0 |
| 1279 | Serious | 2019-07-04 | 72.0 |
| 1280 | Serious | 2019-07-05 | 66.0 |
| 1281 | Serious | 2019-07-06 | 63.0 |
| 1282 | Serious | 2019-07-07 | 69.0 |
| 1283 | Serious | 2019-07-08 | 56.0 |
| 1284 | Serious | 2019-07-09 | 44.0 |
| 1285 | Serious | 2019-07-10 | 67.0 |
| 1286 | Serious | 2019-07-11 | 71.0 |
| 1287 | Serious | 2019-07-12 | 65.0 |
| 1288 | Serious | 2019-07-13 | 60.0 |
| 1289 | Serious | 2019-07-14 | 56.0 |
| 1290 | Serious | 2019-07-15 | 54.0 |
| 1291 | Serious | 2019-07-16 | 60.0 |
| 1292 | Serious | 2019-07-17 | 77.0 |
| 1293 | Serious | 2019-07-18 | 69.0 |
| 1294 | Serious | 2019-07-19 | 68.0 |
| 1295 | Serious | 2019-07-20 | 54.0 |
| 1296 | Serious | 2019-07-21 | 54.0 |
| 1297 | Serious | 2019-07-22 | 58.0 |
| 1298 | Serious | 2019-07-23 | 70.0 |
| 1299 | Serious | 2019-07-24 | 80.0 |
| 1300 | Serious | 2019-07-25 | 75.0 |
| 1301 | Serious | 2019-07-26 | 76.0 |
| 1302 | Serious | 2019-07-27 | 60.0 |
| 1303 | Serious | 2019-07-28 | 46.0 |
| 1304 | Serious | 2019-07-29 | 75.0 |
| 1305 | Serious | 2019-07-30 | 45.0 |
| 1306 | Serious | 2019-07-31 | 47.0 |
| 1307 | Serious | 2019-08-01 | 69.0 |
| 1308 | Serious | 2019-08-02 | 67.0 |
| 1309 | Serious | 2019-08-03 | 73.0 |
| 1310 | Serious | 2019-08-04 | 72.0 |
| 1311 | Serious | 2019-08-05 | 63.0 |
| 1312 | Serious | 2019-08-06 | 66.0 |
| 1313 | Serious | 2019-08-07 | 52.0 |
| 1314 | Serious | 2019-08-08 | 55.0 |
| 1315 | Serious | 2019-08-09 | 60.0 |
| 1316 | Serious | 2019-08-10 | 55.0 |
| 1317 | Serious | 2019-08-11 | 58.0 |
| 1318 | Serious | 2019-08-12 | 45.0 |
| 1319 | Serious | 2019-08-13 | 59.0 |
| 1320 | Serious | 2019-08-14 | 51.0 |
| 1321 | Serious | 2019-08-15 | 62.0 |
| 1322 | Serious | 2019-08-16 | 52.0 |
| 1323 | Serious | 2019-08-17 | 57.0 |
| 1324 | Serious | 2019-08-18 | 53.0 |
| 1325 | Serious | 2019-08-19 | 68.0 |
| 1326 | Serious | 2019-08-20 | 53.0 |
| 1327 | Serious | 2019-08-21 | 59.0 |
| 1328 | Serious | 2019-08-22 | 60.0 |
| 1329 | Serious | 2019-08-23 | 75.0 |
| 1330 | Serious | 2019-08-24 | 73.0 |
| 1331 | Serious | 2019-08-25 | 89.0 |
| 1332 | Serious | 2019-08-26 | 48.0 |
| 1333 | Serious | 2019-08-27 | 65.0 |
| 1334 | Serious | 2019-08-28 | 54.0 |
| 1335 | Serious | 2019-08-29 | 64.0 |
| 1336 | Serious | 2019-08-30 | 52.0 |
| 1337 | Serious | 2019-08-31 | 50.0 |
| 1338 | Serious | 2019-09-01 | 56.0 |
| 1339 | Serious | 2019-09-02 | 58.0 |
| 1340 | Serious | 2019-09-03 | 53.0 |
| 1341 | Serious | 2019-09-04 | 56.0 |
| 1342 | Serious | 2019-09-05 | 72.0 |
| 1343 | Serious | 2019-09-06 | 70.0 |
| 1344 | Serious | 2019-09-07 | 65.0 |
| 1345 | Serious | 2019-09-08 | 67.0 |
| 1346 | Serious | 2019-09-09 | 47.0 |
| 1347 | Serious | 2019-09-10 | 67.0 |
| 1348 | Serious | 2019-09-11 | 53.0 |
| 1349 | Serious | 2019-09-12 | 68.0 |
| 1350 | Serious | 2019-09-13 | 82.0 |
| 1351 | Serious | 2019-09-14 | 65.0 |
| 1352 | Serious | 2019-09-15 | 88.0 |
| 1353 | Serious | 2019-09-16 | 58.0 |
| 1354 | Serious | 2019-09-17 | 79.0 |
| 1355 | Serious | 2019-09-18 | 69.0 |
| 1356 | Serious | 2019-09-19 | 77.0 |
| 1357 | Serious | 2019-09-20 | 100.0 |
| 1358 | Serious | 2019-09-21 | 90.0 |
| 1359 | Serious | 2019-09-22 | 54.0 |
| 1360 | Serious | 2019-09-23 | 68.0 |
| 1361 | Serious | 2019-09-24 | 68.0 |
| 1362 | Serious | 2019-09-25 | 66.0 |
| 1363 | Serious | 2019-09-26 | 65.0 |
| 1364 | Serious | 2019-09-27 | 80.0 |
| 1365 | Serious | 2019-09-28 | 53.0 |
| 1366 | Serious | 2019-09-29 | 47.0 |
| 1367 | Serious | 2019-09-30 | 64.0 |
| 1368 | Serious | 2019-10-01 | 61.0 |
| 1369 | Serious | 2019-10-02 | 87.0 |
| 1370 | Serious | 2019-10-03 | 69.0 |
| 1371 | Serious | 2019-10-04 | 63.0 |
| 1372 | Serious | 2019-10-05 | 54.0 |
| 1373 | Serious | 2019-10-06 | 42.0 |
| 1374 | Serious | 2019-10-07 | 59.0 |
| 1375 | Serious | 2019-10-08 | 52.0 |
| 1376 | Serious | 2019-10-09 | 50.0 |
| 1377 | Serious | 2019-10-10 | 70.0 |
| 1378 | Serious | 2019-10-11 | 61.0 |
| 1379 | Serious | 2019-10-12 | 64.0 |
| 1380 | Serious | 2019-10-13 | 47.0 |
| 1381 | Serious | 2019-10-14 | 54.0 |
| 1382 | Serious | 2019-10-15 | 72.0 |
| 1383 | Serious | 2019-10-16 | 84.0 |
| 1384 | Serious | 2019-10-17 | 75.0 |
| 1385 | Serious | 2019-10-18 | 76.0 |
| 1386 | Serious | 2019-10-19 | 56.0 |
| 1387 | Serious | 2019-10-20 | 40.0 |
| 1388 | Serious | 2019-10-21 | 54.0 |
| 1389 | Serious | 2019-10-22 | 51.0 |
| 1390 | Serious | 2019-10-23 | 55.0 |
| 1391 | Serious | 2019-10-24 | 59.0 |
| 1392 | Serious | 2019-10-25 | 72.0 |
| 1393 | Serious | 2019-10-26 | 60.0 |
| 1394 | Serious | 2019-10-27 | 60.0 |
| 1395 | Serious | 2019-10-28 | 49.0 |
| 1396 | Serious | 2019-10-29 | 48.0 |
| 1397 | Serious | 2019-10-30 | 74.0 |
| 1398 | Serious | 2019-10-31 | 53.0 |
| 1399 | Serious | 2019-11-01 | 67.0 |
| 1400 | Serious | 2019-11-02 | 51.0 |
| 1401 | Serious | 2019-11-03 | 38.0 |
| 1402 | Serious | 2019-11-04 | 55.0 |
| 1403 | Serious | 2019-11-05 | 65.0 |
| 1404 | Serious | 2019-11-06 | 59.0 |
| 1405 | Serious | 2019-11-07 | 56.0 |
| 1406 | Serious | 2019-11-08 | 70.0 |
| 1407 | Serious | 2019-11-09 | 58.0 |
| 1408 | Serious | 2019-11-10 | 56.0 |
| 1409 | Serious | 2019-11-11 | 64.0 |
| 1410 | Serious | 2019-11-12 | 63.0 |
| 1411 | Serious | 2019-11-13 | 64.0 |
| 1412 | Serious | 2019-11-14 | 64.0 |
| 1413 | Serious | 2019-11-15 | 84.0 |
| 1414 | Serious | 2019-11-16 | 60.0 |
| 1415 | Serious | 2019-11-17 | 46.0 |
| 1416 | Serious | 2019-11-18 | 67.0 |
| 1417 | Serious | 2019-11-19 | 66.0 |
| 1418 | Serious | 2019-11-20 | 64.0 |
| 1419 | Serious | 2019-11-21 | 64.0 |
| 1420 | Serious | 2019-11-22 | 68.0 |
| 1421 | Serious | 2019-11-23 | 52.0 |
| 1422 | Serious | 2019-11-24 | 47.0 |
| 1423 | Serious | 2019-11-25 | 69.0 |
| 1424 | Serious | 2019-11-26 | 60.0 |
| 1425 | Serious | 2019-11-27 | 70.0 |
| 1426 | Serious | 2019-11-28 | 75.0 |
| 1427 | Serious | 2019-11-29 | 75.0 |
| 1428 | Serious | 2019-11-30 | 47.0 |
| 1429 | Serious | 2019-12-01 | 37.0 |
| 1430 | Serious | 2019-12-02 | 75.0 |
| 1431 | Serious | 2019-12-03 | 86.0 |
| 1432 | Serious | 2019-12-04 | 87.0 |
| 1433 | Serious | 2019-12-05 | 61.0 |
| 1434 | Serious | 2019-12-06 | 63.0 |
| 1435 | Serious | 2019-12-07 | 64.0 |
| 1436 | Serious | 2019-12-08 | 54.0 |
| 1437 | Serious | 2019-12-09 | 72.0 |
| 1438 | Serious | 2019-12-10 | 72.0 |
| 1439 | Serious | 2019-12-11 | 76.0 |
| 1440 | Serious | 2019-12-12 | 79.0 |
| 1441 | Serious | 2019-12-13 | 75.0 |
| 1442 | Serious | 2019-12-14 | 60.0 |
| 1443 | Serious | 2019-12-15 | 54.0 |
| 1444 | Serious | 2019-12-16 | 66.0 |
| 1445 | Serious | 2019-12-17 | 56.0 |
| 1446 | Serious | 2019-12-18 | 68.0 |
| 1447 | Serious | 2019-12-19 | 59.0 |
| 1448 | Serious | 2019-12-20 | 73.0 |
| 1449 | Serious | 2019-12-21 | 53.0 |
| 1450 | Serious | 2019-12-22 | 55.0 |
| 1451 | Serious | 2019-12-23 | 60.0 |
| 1452 | Serious | 2019-12-24 | 41.0 |
| 1453 | Serious | 2019-12-25 | 24.0 |
| 1454 | Serious | 2019-12-26 | 32.0 |
| 1455 | Serious | 2019-12-27 | 39.0 |
| 1456 | Serious | 2019-12-28 | 28.0 |
| 1457 | Serious | 2019-12-29 | 33.0 |
| 1458 | Serious | 2019-12-30 | 44.0 |
| 1459 | Serious | 2019-12-31 | 42.0 |
| 1460 | Slight | 2018-01-01 | 158.0 |
| 1461 | Slight | 2018-01-02 | 159.0 |
| 1462 | Slight | 2018-01-03 | 196.0 |
| 1463 | Slight | 2018-01-04 | 215.0 |
| 1464 | Slight | 2018-01-05 | 273.0 |
| 1465 | Slight | 2018-01-06 | 215.0 |
| 1466 | Slight | 2018-01-07 | 196.0 |
| 1467 | Slight | 2018-01-08 | 246.0 |
| 1468 | Slight | 2018-01-09 | 247.0 |
| 1469 | Slight | 2018-01-10 | 323.0 |
| 1470 | Slight | 2018-01-11 | 284.0 |
| 1471 | Slight | 2018-01-12 | 305.0 |
| 1472 | Slight | 2018-01-13 | 199.0 |
| 1473 | Slight | 2018-01-14 | 160.0 |
| 1474 | Slight | 2018-01-15 | 255.0 |
| 1475 | Slight | 2018-01-16 | 312.0 |
| 1476 | Slight | 2018-01-17 | 272.0 |
| 1477 | Slight | 2018-01-18 | 296.0 |
| 1478 | Slight | 2018-01-19 | 397.0 |
| 1479 | Slight | 2018-01-20 | 216.0 |
| 1480 | Slight | 2018-01-21 | 190.0 |
| 1481 | Slight | 2018-01-22 | 241.0 |
| 1482 | Slight | 2018-01-23 | 243.0 |
| 1483 | Slight | 2018-01-24 | 280.0 |
| 1484 | Slight | 2018-01-25 | 305.0 |
| 1485 | Slight | 2018-01-26 | 314.0 |
| 1486 | Slight | 2018-01-27 | 240.0 |
| 1487 | Slight | 2018-01-28 | 175.0 |
| 1488 | Slight | 2018-01-29 | 279.0 |
| 1489 | Slight | 2018-01-30 | 310.0 |
| 1490 | Slight | 2018-01-31 | 300.0 |
| 1491 | Slight | 2018-02-01 | 303.0 |
| 1492 | Slight | 2018-02-02 | 330.0 |
| 1493 | Slight | 2018-02-03 | 222.0 |
| 1494 | Slight | 2018-02-04 | 178.0 |
| 1495 | Slight | 2018-02-05 | 262.0 |
| 1496 | Slight | 2018-02-06 | 241.0 |
| 1497 | Slight | 2018-02-07 | 286.0 |
| 1498 | Slight | 2018-02-08 | 299.0 |
| 1499 | Slight | 2018-02-09 | 313.0 |
| 1500 | Slight | 2018-02-10 | 216.0 |
| 1501 | Slight | 2018-02-11 | 206.0 |
| 1502 | Slight | 2018-02-12 | 288.0 |
| 1503 | Slight | 2018-02-13 | 287.0 |
| 1504 | Slight | 2018-02-14 | 274.0 |
| 1505 | Slight | 2018-02-15 | 289.0 |
| 1506 | Slight | 2018-02-16 | 322.0 |
| 1507 | Slight | 2018-02-17 | 260.0 |
| 1508 | Slight | 2018-02-18 | 206.0 |
| 1509 | Slight | 2018-02-19 | 240.0 |
| 1510 | Slight | 2018-02-20 | 257.0 |
| 1511 | Slight | 2018-02-21 | 235.0 |
| 1512 | Slight | 2018-02-22 | 241.0 |
| 1513 | Slight | 2018-02-23 | 265.0 |
| 1514 | Slight | 2018-02-24 | 226.0 |
| 1515 | Slight | 2018-02-25 | 176.0 |
| 1516 | Slight | 2018-02-26 | 189.0 |
| 1517 | Slight | 2018-02-27 | 251.0 |
| 1518 | Slight | 2018-02-28 | 238.0 |
| 1519 | Slight | 2018-03-01 | 190.0 |
| 1520 | Slight | 2018-03-02 | 196.0 |
| 1521 | Slight | 2018-03-03 | 129.0 |
| 1522 | Slight | 2018-03-04 | 123.0 |
| 1523 | Slight | 2018-03-05 | 203.0 |
| 1524 | Slight | 2018-03-06 | 217.0 |
| 1525 | Slight | 2018-03-07 | 259.0 |
| 1526 | Slight | 2018-03-08 | 266.0 |
| 1527 | Slight | 2018-03-09 | 317.0 |
| 1528 | Slight | 2018-03-10 | 218.0 |
| 1529 | Slight | 2018-03-11 | 183.0 |
| 1530 | Slight | 2018-03-12 | 233.0 |
| 1531 | Slight | 2018-03-13 | 265.0 |
| 1532 | Slight | 2018-03-14 | 219.0 |
| 1533 | Slight | 2018-03-15 | 237.0 |
| 1534 | Slight | 2018-03-16 | 259.0 |
| 1535 | Slight | 2018-03-17 | 225.0 |
| 1536 | Slight | 2018-03-18 | 169.0 |
| 1537 | Slight | 2018-03-19 | 233.0 |
| 1538 | Slight | 2018-03-20 | 253.0 |
| 1539 | Slight | 2018-03-21 | 279.0 |
| 1540 | Slight | 2018-03-22 | 231.0 |
| 1541 | Slight | 2018-03-23 | 277.0 |
| 1542 | Slight | 2018-03-24 | 196.0 |
| 1543 | Slight | 2018-03-25 | 157.0 |
| 1544 | Slight | 2018-03-26 | 257.0 |
| 1545 | Slight | 2018-03-27 | 239.0 |
| 1546 | Slight | 2018-03-28 | 230.0 |
| 1547 | Slight | 2018-03-29 | 286.0 |
| 1548 | Slight | 2018-03-30 | 200.0 |
| 1549 | Slight | 2018-03-31 | 158.0 |
| 1550 | Slight | 2018-04-01 | 161.0 |
| 1551 | Slight | 2018-04-02 | 131.0 |
| 1552 | Slight | 2018-04-03 | 195.0 |
| 1553 | Slight | 2018-04-04 | 182.0 |
| 1554 | Slight | 2018-04-05 | 266.0 |
| 1555 | Slight | 2018-04-06 | 210.0 |
| 1556 | Slight | 2018-04-07 | 196.0 |
| 1557 | Slight | 2018-04-08 | 148.0 |
| 1558 | Slight | 2018-04-09 | 224.0 |
| 1559 | Slight | 2018-04-10 | 185.0 |
| 1560 | Slight | 2018-04-11 | 196.0 |
| 1561 | Slight | 2018-04-12 | 183.0 |
| 1562 | Slight | 2018-04-13 | 211.0 |
| 1563 | Slight | 2018-04-14 | 232.0 |
| 1564 | Slight | 2018-04-15 | 160.0 |
| 1565 | Slight | 2018-04-16 | 257.0 |
| 1566 | Slight | 2018-04-17 | 210.0 |
| 1567 | Slight | 2018-04-18 | 329.0 |
| 1568 | Slight | 2018-04-19 | 334.0 |
| 1569 | Slight | 2018-04-20 | 334.0 |
| 1570 | Slight | 2018-04-21 | 297.0 |
| 1571 | Slight | 2018-04-22 | 200.0 |
| 1572 | Slight | 2018-04-23 | 232.0 |
| 1573 | Slight | 2018-04-24 | 231.0 |
| 1574 | Slight | 2018-04-25 | 278.0 |
| 1575 | Slight | 2018-04-26 | 281.0 |
| 1576 | Slight | 2018-04-27 | 311.0 |
| 1577 | Slight | 2018-04-28 | 208.0 |
| 1578 | Slight | 2018-04-29 | 155.0 |
| 1579 | Slight | 2018-04-30 | 261.0 |
| 1580 | Slight | 2018-05-01 | 271.0 |
| 1581 | Slight | 2018-05-02 | 295.0 |
| 1582 | Slight | 2018-05-03 | 251.0 |
| 1583 | Slight | 2018-05-04 | 278.0 |
| 1584 | Slight | 2018-05-05 | 260.0 |
| 1585 | Slight | 2018-05-06 | 250.0 |
| 1586 | Slight | 2018-05-07 | 231.0 |
| 1587 | Slight | 2018-05-08 | 365.0 |
| 1588 | Slight | 2018-05-09 | 282.0 |
| 1589 | Slight | 2018-05-10 | 314.0 |
| 1590 | Slight | 2018-05-11 | 304.0 |
| 1591 | Slight | 2018-05-12 | 232.0 |
| 1592 | Slight | 2018-05-13 | 208.0 |
| 1593 | Slight | 2018-05-14 | 300.0 |
| 1594 | Slight | 2018-05-15 | 290.0 |
| 1595 | Slight | 2018-05-16 | 259.0 |
| 1596 | Slight | 2018-05-17 | 321.0 |
| 1597 | Slight | 2018-05-18 | 342.0 |
| 1598 | Slight | 2018-05-19 | 276.0 |
| 1599 | Slight | 2018-05-20 | 217.0 |
| 1600 | Slight | 2018-05-21 | 310.0 |
| 1601 | Slight | 2018-05-22 | 307.0 |
| 1602 | Slight | 2018-05-23 | 287.0 |
| 1603 | Slight | 2018-05-24 | 253.0 |
| 1604 | Slight | 2018-05-25 | 313.0 |
| 1605 | Slight | 2018-05-26 | 252.0 |
| 1606 | Slight | 2018-05-27 | 199.0 |
| 1607 | Slight | 2018-05-28 | 215.0 |
| 1608 | Slight | 2018-05-29 | 232.0 |
| 1609 | Slight | 2018-05-30 | 222.0 |
| 1610 | Slight | 2018-05-31 | 216.0 |
| 1611 | Slight | 2018-06-01 | 251.0 |
| 1612 | Slight | 2018-06-02 | 200.0 |
| 1613 | Slight | 2018-06-03 | 199.0 |
| 1614 | Slight | 2018-06-04 | 229.0 |
| 1615 | Slight | 2018-06-05 | 256.0 |
| 1616 | Slight | 2018-06-06 | 311.0 |
| 1617 | Slight | 2018-06-07 | 278.0 |
| 1618 | Slight | 2018-06-08 | 256.0 |
| 1619 | Slight | 2018-06-09 | 244.0 |
| 1620 | Slight | 2018-06-10 | 231.0 |
| 1621 | Slight | 2018-06-11 | 293.0 |
| 1622 | Slight | 2018-06-12 | 257.0 |
| 1623 | Slight | 2018-06-13 | 258.0 |
| 1624 | Slight | 2018-06-14 | 307.0 |
| 1625 | Slight | 2018-06-15 | 283.0 |
| 1626 | Slight | 2018-06-16 | 246.0 |
| 1627 | Slight | 2018-06-17 | 194.0 |
| 1628 | Slight | 2018-06-18 | 264.0 |
| 1629 | Slight | 2018-06-19 | 239.0 |
| 1630 | Slight | 2018-06-20 | 283.0 |
| 1631 | Slight | 2018-06-21 | 338.0 |
| 1632 | Slight | 2018-06-22 | 334.0 |
| 1633 | Slight | 2018-06-23 | 227.0 |
| 1634 | Slight | 2018-06-24 | 240.0 |
| 1635 | Slight | 2018-06-25 | 357.0 |
| 1636 | Slight | 2018-06-26 | 340.0 |
| 1637 | Slight | 2018-06-27 | 377.0 |
| 1638 | Slight | 2018-06-28 | 305.0 |
| 1639 | Slight | 2018-06-29 | 375.0 |
| 1640 | Slight | 2018-06-30 | 276.0 |
| 1641 | Slight | 2018-07-01 | 219.0 |
| 1642 | Slight | 2018-07-02 | 330.0 |
| 1643 | Slight | 2018-07-03 | 371.0 |
| 1644 | Slight | 2018-07-04 | 312.0 |
| 1645 | Slight | 2018-07-05 | 303.0 |
| 1646 | Slight | 2018-07-06 | 300.0 |
| 1647 | Slight | 2018-07-07 | 267.0 |
| 1648 | Slight | 2018-07-08 | 231.0 |
| 1649 | Slight | 2018-07-09 | 306.0 |
| 1650 | Slight | 2018-07-10 | 286.0 |
| 1651 | Slight | 2018-07-11 | 304.0 |
| 1652 | Slight | 2018-07-12 | 290.0 |
| 1653 | Slight | 2018-07-13 | 281.0 |
| 1654 | Slight | 2018-07-14 | 236.0 |
| 1655 | Slight | 2018-07-15 | 212.0 |
| 1656 | Slight | 2018-07-16 | 269.0 |
| 1657 | Slight | 2018-07-17 | 255.0 |
| 1658 | Slight | 2018-07-18 | 253.0 |
| 1659 | Slight | 2018-07-19 | 286.0 |
| 1660 | Slight | 2018-07-20 | 256.0 |
| 1661 | Slight | 2018-07-21 | 223.0 |
| 1662 | Slight | 2018-07-22 | 188.0 |
| 1663 | Slight | 2018-07-23 | 243.0 |
| 1664 | Slight | 2018-07-24 | 272.0 |
| 1665 | Slight | 2018-07-25 | 267.0 |
| 1666 | Slight | 2018-07-26 | 288.0 |
| 1667 | Slight | 2018-07-27 | 307.0 |
| 1668 | Slight | 2018-07-28 | 229.0 |
| 1669 | Slight | 2018-07-29 | 210.0 |
| 1670 | Slight | 2018-07-30 | 257.0 |
| 1671 | Slight | 2018-07-31 | 250.0 |
| 1672 | Slight | 2018-08-01 | 235.0 |
| 1673 | Slight | 2018-08-02 | 241.0 |
| 1674 | Slight | 2018-08-03 | 248.0 |
| 1675 | Slight | 2018-08-04 | 219.0 |
| 1676 | Slight | 2018-08-05 | 238.0 |
| 1677 | Slight | 2018-08-06 | 256.0 |
| 1678 | Slight | 2018-08-07 | 249.0 |
| 1679 | Slight | 2018-08-08 | 269.0 |
| 1680 | Slight | 2018-08-09 | 282.0 |
| 1681 | Slight | 2018-08-10 | 308.0 |
| 1682 | Slight | 2018-08-11 | 202.0 |
| 1683 | Slight | 2018-08-12 | 170.0 |
| 1684 | Slight | 2018-08-13 | 245.0 |
| 1685 | Slight | 2018-08-14 | 207.0 |
| 1686 | Slight | 2018-08-15 | 225.0 |
| 1687 | Slight | 2018-08-16 | 216.0 |
| 1688 | Slight | 2018-08-17 | 252.0 |
| 1689 | Slight | 2018-08-18 | 190.0 |
| 1690 | Slight | 2018-08-19 | 167.0 |
| 1691 | Slight | 2018-08-20 | 217.0 |
| 1692 | Slight | 2018-08-21 | 231.0 |
| 1693 | Slight | 2018-08-22 | 242.0 |
| 1694 | Slight | 2018-08-23 | 253.0 |
| 1695 | Slight | 2018-08-24 | 244.0 |
| 1696 | Slight | 2018-08-25 | 228.0 |
| 1697 | Slight | 2018-08-26 | 225.0 |
| 1698 | Slight | 2018-08-27 | 152.0 |
| 1699 | Slight | 2018-08-28 | 200.0 |
| 1700 | Slight | 2018-08-29 | 203.0 |
| 1701 | Slight | 2018-08-30 | 235.0 |
| 1702 | Slight | 2018-08-31 | 293.0 |
| 1703 | Slight | 2018-09-01 | 243.0 |
| 1704 | Slight | 2018-09-02 | 245.0 |
| 1705 | Slight | 2018-09-03 | 242.0 |
| 1706 | Slight | 2018-09-04 | 228.0 |
| 1707 | Slight | 2018-09-05 | 235.0 |
| 1708 | Slight | 2018-09-06 | 280.0 |
| 1709 | Slight | 2018-09-07 | 332.0 |
| 1710 | Slight | 2018-09-08 | 211.0 |
| 1711 | Slight | 2018-09-09 | 174.0 |
| 1712 | Slight | 2018-09-10 | 222.0 |
| 1713 | Slight | 2018-09-11 | 257.0 |
| 1714 | Slight | 2018-09-12 | 273.0 |
| 1715 | Slight | 2018-09-13 | 319.0 |
| 1716 | Slight | 2018-09-14 | 316.0 |
| 1717 | Slight | 2018-09-15 | 244.0 |
| 1718 | Slight | 2018-09-16 | 206.0 |
| 1719 | Slight | 2018-09-17 | 281.0 |
| 1720 | Slight | 2018-09-18 | 269.0 |
| 1721 | Slight | 2018-09-19 | 269.0 |
| 1722 | Slight | 2018-09-20 | 294.0 |
| 1723 | Slight | 2018-09-21 | 305.0 |
| 1724 | Slight | 2018-09-22 | 262.0 |
| 1725 | Slight | 2018-09-23 | 226.0 |
| 1726 | Slight | 2018-09-24 | 312.0 |
| 1727 | Slight | 2018-09-25 | 306.0 |
| 1728 | Slight | 2018-09-26 | 289.0 |
| 1729 | Slight | 2018-09-27 | 317.0 |
| 1730 | Slight | 2018-09-28 | 305.0 |
| 1731 | Slight | 2018-09-29 | 272.0 |
| 1732 | Slight | 2018-09-30 | 171.0 |
| 1733 | Slight | 2018-10-01 | 297.0 |
| 1734 | Slight | 2018-10-02 | 232.0 |
| 1735 | Slight | 2018-10-03 | 264.0 |
| 1736 | Slight | 2018-10-04 | 269.0 |
| 1737 | Slight | 2018-10-05 | 298.0 |
| 1738 | Slight | 2018-10-06 | 265.0 |
| 1739 | Slight | 2018-10-07 | 185.0 |
| 1740 | Slight | 2018-10-08 | 266.0 |
| 1741 | Slight | 2018-10-09 | 293.0 |
| 1742 | Slight | 2018-10-10 | 359.0 |
| 1743 | Slight | 2018-10-11 | 269.0 |
| 1744 | Slight | 2018-10-12 | 287.0 |
| 1745 | Slight | 2018-10-13 | 220.0 |
| 1746 | Slight | 2018-10-14 | 228.0 |
| 1747 | Slight | 2018-10-15 | 293.0 |
| 1748 | Slight | 2018-10-16 | 287.0 |
| 1749 | Slight | 2018-10-17 | 259.0 |
| 1750 | Slight | 2018-10-18 | 257.0 |
| 1751 | Slight | 2018-10-19 | 324.0 |
| 1752 | Slight | 2018-10-20 | 236.0 |
| 1753 | Slight | 2018-10-21 | 216.0 |
| 1754 | Slight | 2018-10-22 | 277.0 |
| 1755 | Slight | 2018-10-23 | 248.0 |
| 1756 | Slight | 2018-10-24 | 252.0 |
| 1757 | Slight | 2018-10-25 | 246.0 |
| 1758 | Slight | 2018-10-26 | 342.0 |
| 1759 | Slight | 2018-10-27 | 238.0 |
| 1760 | Slight | 2018-10-28 | 187.0 |
| 1761 | Slight | 2018-10-29 | 288.0 |
| 1762 | Slight | 2018-10-30 | 231.0 |
| 1763 | Slight | 2018-10-31 | 297.0 |
| 1764 | Slight | 2018-11-01 | 290.0 |
| 1765 | Slight | 2018-11-02 | 389.0 |
| 1766 | Slight | 2018-11-03 | 205.0 |
| 1767 | Slight | 2018-11-04 | 176.0 |
| 1768 | Slight | 2018-11-05 | 266.0 |
| 1769 | Slight | 2018-11-06 | 282.0 |
| 1770 | Slight | 2018-11-07 | 313.0 |
| 1771 | Slight | 2018-11-08 | 322.0 |
| 1772 | Slight | 2018-11-09 | 318.0 |
| 1773 | Slight | 2018-11-10 | 277.0 |
| 1774 | Slight | 2018-11-11 | 241.0 |
| 1775 | Slight | 2018-11-12 | 326.0 |
| 1776 | Slight | 2018-11-13 | 321.0 |
| 1777 | Slight | 2018-11-14 | 302.0 |
| 1778 | Slight | 2018-11-15 | 282.0 |
| 1779 | Slight | 2018-11-16 | 288.0 |
| 1780 | Slight | 2018-11-17 | 223.0 |
| 1781 | Slight | 2018-11-18 | 190.0 |
| 1782 | Slight | 2018-11-19 | 294.0 |
| 1783 | Slight | 2018-11-20 | 299.0 |
| 1784 | Slight | 2018-11-21 | 304.0 |
| 1785 | Slight | 2018-11-22 | 311.0 |
| 1786 | Slight | 2018-11-23 | 314.0 |
| 1787 | Slight | 2018-11-24 | 238.0 |
| 1788 | Slight | 2018-11-25 | 171.0 |
| 1789 | Slight | 2018-11-26 | 276.0 |
| 1790 | Slight | 2018-11-27 | 345.0 |
| 1791 | Slight | 2018-11-28 | 317.0 |
| 1792 | Slight | 2018-11-29 | 286.0 |
| 1793 | Slight | 2018-11-30 | 371.0 |
| 1794 | Slight | 2018-12-01 | 264.0 |
| 1795 | Slight | 2018-12-02 | 200.0 |
| 1796 | Slight | 2018-12-03 | 304.0 |
| 1797 | Slight | 2018-12-04 | 378.0 |
| 1798 | Slight | 2018-12-05 | 329.0 |
| 1799 | Slight | 2018-12-06 | 310.0 |
| 1800 | Slight | 2018-12-07 | 329.0 |
| 1801 | Slight | 2018-12-08 | 218.0 |
| 1802 | Slight | 2018-12-09 | 186.0 |
| 1803 | Slight | 2018-12-10 | 275.0 |
| 1804 | Slight | 2018-12-11 | 313.0 |
| 1805 | Slight | 2018-12-12 | 301.0 |
| 1806 | Slight | 2018-12-13 | 281.0 |
| 1807 | Slight | 2018-12-14 | 294.0 |
| 1808 | Slight | 2018-12-15 | 270.0 |
| 1809 | Slight | 2018-12-16 | 224.0 |
| 1810 | Slight | 2018-12-17 | 298.0 |
| 1811 | Slight | 2018-12-18 | 320.0 |
| 1812 | Slight | 2018-12-19 | 323.0 |
| 1813 | Slight | 2018-12-20 | 249.0 |
| 1814 | Slight | 2018-12-21 | 284.0 |
| 1815 | Slight | 2018-12-22 | 216.0 |
| 1816 | Slight | 2018-12-23 | 203.0 |
| 1817 | Slight | 2018-12-24 | 201.0 |
| 1818 | Slight | 2018-12-25 | 91.0 |
| 1819 | Slight | 2018-12-26 | 128.0 |
| 1820 | Slight | 2018-12-27 | 176.0 |
| 1821 | Slight | 2018-12-28 | 165.0 |
| 1822 | Slight | 2018-12-29 | 163.0 |
| 1823 | Slight | 2018-12-30 | 145.0 |
| 1824 | Slight | 2018-12-31 | 142.0 |
| 1825 | Slight | 2019-01-01 | 157.0 |
| 1826 | Slight | 2019-01-02 | 155.0 |
| 1827 | Slight | 2019-01-03 | 153.0 |
| 1828 | Slight | 2019-01-04 | 183.0 |
| 1829 | Slight | 2019-01-05 | 138.0 |
| 1830 | Slight | 2019-01-06 | 141.0 |
| 1831 | Slight | 2019-01-07 | 233.0 |
| 1832 | Slight | 2019-01-08 | 264.0 |
| 1833 | Slight | 2019-01-09 | 286.0 |
| 1834 | Slight | 2019-01-10 | 266.0 |
| 1835 | Slight | 2019-01-11 | 251.0 |
| 1836 | Slight | 2019-01-12 | 217.0 |
| 1837 | Slight | 2019-01-13 | 126.0 |
| 1838 | Slight | 2019-01-14 | 239.0 |
| 1839 | Slight | 2019-01-15 | 269.0 |
| 1840 | Slight | 2019-01-16 | 288.0 |
| 1841 | Slight | 2019-01-17 | 299.0 |
| 1842 | Slight | 2019-01-18 | 242.0 |
| 1843 | Slight | 2019-01-19 | 186.0 |
| 1844 | Slight | 2019-01-20 | 152.0 |
| 1845 | Slight | 2019-01-21 | 249.0 |
| 1846 | Slight | 2019-01-22 | 339.0 |
| 1847 | Slight | 2019-01-23 | 278.0 |
| 1848 | Slight | 2019-01-24 | 266.0 |
| 1849 | Slight | 2019-01-25 | 290.0 |
| 1850 | Slight | 2019-01-26 | 187.0 |
| 1851 | Slight | 2019-01-27 | 153.0 |
| 1852 | Slight | 2019-01-28 | 258.0 |
| 1853 | Slight | 2019-01-29 | 279.0 |
| 1854 | Slight | 2019-01-30 | 312.0 |
| 1855 | Slight | 2019-01-31 | 245.0 |
| 1856 | Slight | 2019-02-01 | 256.0 |
| 1857 | Slight | 2019-02-02 | 210.0 |
| 1858 | Slight | 2019-02-03 | 191.0 |
| 1859 | Slight | 2019-02-04 | 267.0 |
| 1860 | Slight | 2019-02-05 | 230.0 |
| 1861 | Slight | 2019-02-06 | 241.0 |
| 1862 | Slight | 2019-02-07 | 264.0 |
| 1863 | Slight | 2019-02-08 | 271.0 |
| 1864 | Slight | 2019-02-09 | 195.0 |
| 1865 | Slight | 2019-02-10 | 180.0 |
| 1866 | Slight | 2019-02-11 | 229.0 |
| 1867 | Slight | 2019-02-12 | 228.0 |
| 1868 | Slight | 2019-02-13 | 241.0 |
| 1869 | Slight | 2019-02-14 | 302.0 |
| 1870 | Slight | 2019-02-15 | 304.0 |
| 1871 | Slight | 2019-02-16 | 211.0 |
| 1872 | Slight | 2019-02-17 | 181.0 |
| 1873 | Slight | 2019-02-18 | 214.0 |
| 1874 | Slight | 2019-02-19 | 238.0 |
| 1875 | Slight | 2019-02-20 | 227.0 |
| 1876 | Slight | 2019-02-21 | 240.0 |
| 1877 | Slight | 2019-02-22 | 247.0 |
| 1878 | Slight | 2019-02-23 | 235.0 |
| 1879 | Slight | 2019-02-24 | 201.0 |
| 1880 | Slight | 2019-02-25 | 291.0 |
| 1881 | Slight | 2019-02-26 | 286.0 |
| 1882 | Slight | 2019-02-27 | 275.0 |
| 1883 | Slight | 2019-02-28 | 243.0 |
| 1884 | Slight | 2019-03-01 | 223.0 |
| 1885 | Slight | 2019-03-02 | 216.0 |
| 1886 | Slight | 2019-03-03 | 138.0 |
| 1887 | Slight | 2019-03-04 | 235.0 |
| 1888 | Slight | 2019-03-05 | 282.0 |
| 1889 | Slight | 2019-03-06 | 246.0 |
| 1890 | Slight | 2019-03-07 | 226.0 |
| 1891 | Slight | 2019-03-08 | 317.0 |
| 1892 | Slight | 2019-03-09 | 194.0 |
| 1893 | Slight | 2019-03-10 | 175.0 |
| 1894 | Slight | 2019-03-11 | 259.0 |
| 1895 | Slight | 2019-03-12 | 246.0 |
| 1896 | Slight | 2019-03-13 | 224.0 |
| 1897 | Slight | 2019-03-14 | 233.0 |
| 1898 | Slight | 2019-03-15 | 246.0 |
| 1899 | Slight | 2019-03-16 | 206.0 |
| 1900 | Slight | 2019-03-17 | 184.0 |
| 1901 | Slight | 2019-03-18 | 221.0 |
| 1902 | Slight | 2019-03-19 | 215.0 |
| 1903 | Slight | 2019-03-20 | 235.0 |
| 1904 | Slight | 2019-03-21 | 218.0 |
| 1905 | Slight | 2019-03-22 | 246.0 |
| 1906 | Slight | 2019-03-23 | 211.0 |
| 1907 | Slight | 2019-03-24 | 217.0 |
| 1908 | Slight | 2019-03-25 | 270.0 |
| 1909 | Slight | 2019-03-26 | 263.0 |
| 1910 | Slight | 2019-03-27 | 234.0 |
| 1911 | Slight | 2019-03-28 | 258.0 |
| 1912 | Slight | 2019-03-29 | 305.0 |
| 1913 | Slight | 2019-03-30 | 216.0 |
| 1914 | Slight | 2019-03-31 | 164.0 |
| 1915 | Slight | 2019-04-01 | 240.0 |
| 1916 | Slight | 2019-04-02 | 273.0 |
| 1917 | Slight | 2019-04-03 | 264.0 |
| 1918 | Slight | 2019-04-04 | 274.0 |
| 1919 | Slight | 2019-04-05 | 253.0 |
| 1920 | Slight | 2019-04-06 | 185.0 |
| 1921 | Slight | 2019-04-07 | 178.0 |
| 1922 | Slight | 2019-04-08 | 207.0 |
| 1923 | Slight | 2019-04-09 | 207.0 |
| 1924 | Slight | 2019-04-10 | 246.0 |
| 1925 | Slight | 2019-04-11 | 239.0 |
| 1926 | Slight | 2019-04-12 | 224.0 |
| 1927 | Slight | 2019-04-13 | 198.0 |
| 1928 | Slight | 2019-04-14 | 145.0 |
| 1929 | Slight | 2019-04-15 | 222.0 |
| 1930 | Slight | 2019-04-16 | 177.0 |
| 1931 | Slight | 2019-04-17 | 226.0 |
| 1932 | Slight | 2019-04-18 | 216.0 |
| 1933 | Slight | 2019-04-19 | 204.0 |
| 1934 | Slight | 2019-04-20 | 251.0 |
| 1935 | Slight | 2019-04-21 | 192.0 |
| 1936 | Slight | 2019-04-22 | 169.0 |
| 1937 | Slight | 2019-04-23 | 215.0 |
| 1938 | Slight | 2019-04-24 | 229.0 |
| 1939 | Slight | 2019-04-25 | 232.0 |
| 1940 | Slight | 2019-04-26 | 259.0 |
| 1941 | Slight | 2019-04-27 | 194.0 |
| 1942 | Slight | 2019-04-28 | 160.0 |
| 1943 | Slight | 2019-04-29 | 205.0 |
| 1944 | Slight | 2019-04-30 | 270.0 |
| 1945 | Slight | 2019-05-01 | 243.0 |
| 1946 | Slight | 2019-05-02 | 247.0 |
| 1947 | Slight | 2019-05-03 | 269.0 |
| 1948 | Slight | 2019-05-04 | 230.0 |
| 1949 | Slight | 2019-05-05 | 179.0 |
| 1950 | Slight | 2019-05-06 | 133.0 |
| 1951 | Slight | 2019-05-07 | 244.0 |
| 1952 | Slight | 2019-05-08 | 260.0 |
| 1953 | Slight | 2019-05-09 | 241.0 |
| 1954 | Slight | 2019-05-10 | 271.0 |
| 1955 | Slight | 2019-05-11 | 228.0 |
| 1956 | Slight | 2019-05-12 | 199.0 |
| 1957 | Slight | 2019-05-13 | 307.0 |
| 1958 | Slight | 2019-05-14 | 282.0 |
| 1959 | Slight | 2019-05-15 | 303.0 |
| 1960 | Slight | 2019-05-16 | 286.0 |
| 1961 | Slight | 2019-05-17 | 224.0 |
| 1962 | Slight | 2019-05-18 | 193.0 |
| 1963 | Slight | 2019-05-19 | 146.0 |
| 1964 | Slight | 2019-05-20 | 224.0 |
| 1965 | Slight | 2019-05-21 | 287.0 |
| 1966 | Slight | 2019-05-22 | 251.0 |
| 1967 | Slight | 2019-05-23 | 302.0 |
| 1968 | Slight | 2019-05-24 | 294.0 |
| 1969 | Slight | 2019-05-25 | 213.0 |
| 1970 | Slight | 2019-05-26 | 166.0 |
| 1971 | Slight | 2019-05-27 | 144.0 |
| 1972 | Slight | 2019-05-28 | 237.0 |
| 1973 | Slight | 2019-05-29 | 222.0 |
| 1974 | Slight | 2019-05-30 | 227.0 |
| 1975 | Slight | 2019-05-31 | 211.0 |
| 1976 | Slight | 2019-06-01 | 223.0 |
| 1977 | Slight | 2019-06-02 | 177.0 |
| 1978 | Slight | 2019-06-03 | 249.0 |
| 1979 | Slight | 2019-06-04 | 280.0 |
| 1980 | Slight | 2019-06-05 | 256.0 |
| 1981 | Slight | 2019-06-06 | 290.0 |
| 1982 | Slight | 2019-06-07 | 305.0 |
| 1983 | Slight | 2019-06-08 | 219.0 |
| 1984 | Slight | 2019-06-09 | 186.0 |
| 1985 | Slight | 2019-06-10 | 254.0 |
| 1986 | Slight | 2019-06-11 | 254.0 |
| 1987 | Slight | 2019-06-12 | 276.0 |
| 1988 | Slight | 2019-06-13 | 259.0 |
| 1989 | Slight | 2019-06-14 | 274.0 |
| 1990 | Slight | 2019-06-15 | 237.0 |
| 1991 | Slight | 2019-06-16 | 206.0 |
| 1992 | Slight | 2019-06-17 | 229.0 |
| 1993 | Slight | 2019-06-18 | 274.0 |
| 1994 | Slight | 2019-06-19 | 237.0 |
| 1995 | Slight | 2019-06-20 | 242.0 |
| 1996 | Slight | 2019-06-21 | 312.0 |
| 1997 | Slight | 2019-06-22 | 241.0 |
| 1998 | Slight | 2019-06-23 | 189.0 |
| 1999 | Slight | 2019-06-24 | 242.0 |
| 2000 | Slight | 2019-06-25 | 232.0 |
| 2001 | Slight | 2019-06-26 | 244.0 |
| 2002 | Slight | 2019-06-27 | 301.0 |
| 2003 | Slight | 2019-06-28 | 303.0 |
| 2004 | Slight | 2019-06-29 | 278.0 |
| 2005 | Slight | 2019-06-30 | 214.0 |
| 2006 | Slight | 2019-07-01 | 240.0 |
| 2007 | Slight | 2019-07-02 | 247.0 |
| 2008 | Slight | 2019-07-03 | 289.0 |
| 2009 | Slight | 2019-07-04 | 300.0 |
| 2010 | Slight | 2019-07-05 | 297.0 |
| 2011 | Slight | 2019-07-06 | 254.0 |
| 2012 | Slight | 2019-07-07 | 223.0 |
| 2013 | Slight | 2019-07-08 | 258.0 |
| 2014 | Slight | 2019-07-09 | 228.0 |
| 2015 | Slight | 2019-07-10 | 233.0 |
| 2016 | Slight | 2019-07-11 | 289.0 |
| 2017 | Slight | 2019-07-12 | 297.0 |
| 2018 | Slight | 2019-07-13 | 205.0 |
| 2019 | Slight | 2019-07-14 | 177.0 |
| 2020 | Slight | 2019-07-15 | 289.0 |
| 2021 | Slight | 2019-07-16 | 257.0 |
| 2022 | Slight | 2019-07-17 | 277.0 |
| 2023 | Slight | 2019-07-18 | 254.0 |
| 2024 | Slight | 2019-07-19 | 301.0 |
| 2025 | Slight | 2019-07-20 | 242.0 |
| 2026 | Slight | 2019-07-21 | 200.0 |
| 2027 | Slight | 2019-07-22 | 225.0 |
| 2028 | Slight | 2019-07-23 | 291.0 |
| 2029 | Slight | 2019-07-24 | 252.0 |
| 2030 | Slight | 2019-07-25 | 232.0 |
| 2031 | Slight | 2019-07-26 | 268.0 |
| 2032 | Slight | 2019-07-27 | 228.0 |
| 2033 | Slight | 2019-07-28 | 183.0 |
| 2034 | Slight | 2019-07-29 | 204.0 |
| 2035 | Slight | 2019-07-30 | 225.0 |
| 2036 | Slight | 2019-07-31 | 257.0 |
| 2037 | Slight | 2019-08-01 | 272.0 |
| 2038 | Slight | 2019-08-02 | 256.0 |
| 2039 | Slight | 2019-08-03 | 192.0 |
| 2040 | Slight | 2019-08-04 | 155.0 |
| 2041 | Slight | 2019-08-05 | 218.0 |
| 2042 | Slight | 2019-08-06 | 223.0 |
| 2043 | Slight | 2019-08-07 | 237.0 |
| 2044 | Slight | 2019-08-08 | 244.0 |
| 2045 | Slight | 2019-08-09 | 280.0 |
| 2046 | Slight | 2019-08-10 | 215.0 |
| 2047 | Slight | 2019-08-11 | 171.0 |
| 2048 | Slight | 2019-08-12 | 223.0 |
| 2049 | Slight | 2019-08-13 | 218.0 |
| 2050 | Slight | 2019-08-14 | 236.0 |
| 2051 | Slight | 2019-08-15 | 241.0 |
| 2052 | Slight | 2019-08-16 | 258.0 |
| 2053 | Slight | 2019-08-17 | 228.0 |
| 2054 | Slight | 2019-08-18 | 156.0 |
| 2055 | Slight | 2019-08-19 | 238.0 |
| 2056 | Slight | 2019-08-20 | 216.0 |
| 2057 | Slight | 2019-08-21 | 245.0 |
| 2058 | Slight | 2019-08-22 | 230.0 |
| 2059 | Slight | 2019-08-23 | 297.0 |
| 2060 | Slight | 2019-08-24 | 231.0 |
| 2061 | Slight | 2019-08-25 | 215.0 |
| 2062 | Slight | 2019-08-26 | 197.0 |
| 2063 | Slight | 2019-08-27 | 260.0 |
| 2064 | Slight | 2019-08-28 | 225.0 |
| 2065 | Slight | 2019-08-29 | 228.0 |
| 2066 | Slight | 2019-08-30 | 248.0 |
| 2067 | Slight | 2019-08-31 | 191.0 |
| 2068 | Slight | 2019-09-01 | 198.0 |
| 2069 | Slight | 2019-09-02 | 239.0 |
| 2070 | Slight | 2019-09-03 | 207.0 |
| 2071 | Slight | 2019-09-04 | 253.0 |
| 2072 | Slight | 2019-09-05 | 254.0 |
| 2073 | Slight | 2019-09-06 | 239.0 |
| 2074 | Slight | 2019-09-07 | 207.0 |
| 2075 | Slight | 2019-09-08 | 198.0 |
| 2076 | Slight | 2019-09-09 | 279.0 |
| 2077 | Slight | 2019-09-10 | 247.0 |
| 2078 | Slight | 2019-09-11 | 253.0 |
| 2079 | Slight | 2019-09-12 | 278.0 |
| 2080 | Slight | 2019-09-13 | 306.0 |
| 2081 | Slight | 2019-09-14 | 261.0 |
| 2082 | Slight | 2019-09-15 | 192.0 |
| 2083 | Slight | 2019-09-16 | 227.0 |
| 2084 | Slight | 2019-09-17 | 329.0 |
| 2085 | Slight | 2019-09-18 | 247.0 |
| 2086 | Slight | 2019-09-19 | 301.0 |
| 2087 | Slight | 2019-09-20 | 338.0 |
| 2088 | Slight | 2019-09-21 | 281.0 |
| 2089 | Slight | 2019-09-22 | 198.0 |
| 2090 | Slight | 2019-09-23 | 275.0 |
| 2091 | Slight | 2019-09-24 | 282.0 |
| 2092 | Slight | 2019-09-25 | 267.0 |
| 2093 | Slight | 2019-09-26 | 273.0 |
| 2094 | Slight | 2019-09-27 | 301.0 |
| 2095 | Slight | 2019-09-28 | 240.0 |
| 2096 | Slight | 2019-09-29 | 200.0 |
| 2097 | Slight | 2019-09-30 | 254.0 |
| 2098 | Slight | 2019-10-01 | 290.0 |
| 2099 | Slight | 2019-10-02 | 318.0 |
| 2100 | Slight | 2019-10-03 | 252.0 |
| 2101 | Slight | 2019-10-04 | 313.0 |
| 2102 | Slight | 2019-10-05 | 219.0 |
| 2103 | Slight | 2019-10-06 | 193.0 |
| 2104 | Slight | 2019-10-07 | 226.0 |
| 2105 | Slight | 2019-10-08 | 283.0 |
| 2106 | Slight | 2019-10-09 | 254.0 |
| 2107 | Slight | 2019-10-10 | 265.0 |
| 2108 | Slight | 2019-10-11 | 281.0 |
| 2109 | Slight | 2019-10-12 | 218.0 |
| 2110 | Slight | 2019-10-13 | 190.0 |
| 2111 | Slight | 2019-10-14 | 291.0 |
| 2112 | Slight | 2019-10-15 | 252.0 |
| 2113 | Slight | 2019-10-16 | 297.0 |
| 2114 | Slight | 2019-10-17 | 297.0 |
| 2115 | Slight | 2019-10-18 | 314.0 |
| 2116 | Slight | 2019-10-19 | 237.0 |
| 2117 | Slight | 2019-10-20 | 178.0 |
| 2118 | Slight | 2019-10-21 | 234.0 |
| 2119 | Slight | 2019-10-22 | 227.0 |
| 2120 | Slight | 2019-10-23 | 274.0 |
| 2121 | Slight | 2019-10-24 | 236.0 |
| 2122 | Slight | 2019-10-25 | 275.0 |
| 2123 | Slight | 2019-10-26 | 211.0 |
| 2124 | Slight | 2019-10-27 | 206.0 |
| 2125 | Slight | 2019-10-28 | 249.0 |
| 2126 | Slight | 2019-10-29 | 247.0 |
| 2127 | Slight | 2019-10-30 | 226.0 |
| 2128 | Slight | 2019-10-31 | 275.0 |
| 2129 | Slight | 2019-11-01 | 319.0 |
| 2130 | Slight | 2019-11-02 | 255.0 |
| 2131 | Slight | 2019-11-03 | 193.0 |
| 2132 | Slight | 2019-11-04 | 291.0 |
| 2133 | Slight | 2019-11-05 | 283.0 |
| 2134 | Slight | 2019-11-06 | 282.0 |
| 2135 | Slight | 2019-11-07 | 326.0 |
| 2136 | Slight | 2019-11-08 | 345.0 |
| 2137 | Slight | 2019-11-09 | 259.0 |
| 2138 | Slight | 2019-11-10 | 206.0 |
| 2139 | Slight | 2019-11-11 | 270.0 |
| 2140 | Slight | 2019-11-12 | 299.0 |
| 2141 | Slight | 2019-11-13 | 318.0 |
| 2142 | Slight | 2019-11-14 | 327.0 |
| 2143 | Slight | 2019-11-15 | 319.0 |
| 2144 | Slight | 2019-11-16 | 244.0 |
| 2145 | Slight | 2019-11-17 | 156.0 |
| 2146 | Slight | 2019-11-18 | 314.0 |
| 2147 | Slight | 2019-11-19 | 270.0 |
| 2148 | Slight | 2019-11-20 | 299.0 |
| 2149 | Slight | 2019-11-21 | 306.0 |
| 2150 | Slight | 2019-11-22 | 333.0 |
| 2151 | Slight | 2019-11-23 | 254.0 |
| 2152 | Slight | 2019-11-24 | 168.0 |
| 2153 | Slight | 2019-11-25 | 273.0 |
| 2154 | Slight | 2019-11-26 | 271.0 |
| 2155 | Slight | 2019-11-27 | 352.0 |
| 2156 | Slight | 2019-11-28 | 302.0 |
| 2157 | Slight | 2019-11-29 | 374.0 |
| 2158 | Slight | 2019-11-30 | 291.0 |
| 2159 | Slight | 2019-12-01 | 198.0 |
| 2160 | Slight | 2019-12-02 | 338.0 |
| 2161 | Slight | 2019-12-03 | 324.0 |
| 2162 | Slight | 2019-12-04 | 389.0 |
| 2163 | Slight | 2019-12-05 | 333.0 |
| 2164 | Slight | 2019-12-06 | 315.0 |
| 2165 | Slight | 2019-12-07 | 212.0 |
| 2166 | Slight | 2019-12-08 | 192.0 |
| 2167 | Slight | 2019-12-09 | 263.0 |
| 2168 | Slight | 2019-12-10 | 294.0 |
| 2169 | Slight | 2019-12-11 | 322.0 |
| 2170 | Slight | 2019-12-12 | 326.0 |
| 2171 | Slight | 2019-12-13 | 305.0 |
| 2172 | Slight | 2019-12-14 | 234.0 |
| 2173 | Slight | 2019-12-15 | 179.0 |
| 2174 | Slight | 2019-12-16 | 291.0 |
| 2175 | Slight | 2019-12-17 | 283.0 |
| 2176 | Slight | 2019-12-18 | 318.0 |
| 2177 | Slight | 2019-12-19 | 283.0 |
| 2178 | Slight | 2019-12-20 | 352.0 |
| 2179 | Slight | 2019-12-21 | 255.0 |
| 2180 | Slight | 2019-12-22 | 171.0 |
| 2181 | Slight | 2019-12-23 | 274.0 |
| 2182 | Slight | 2019-12-24 | 201.0 |
| 2183 | Slight | 2019-12-25 | 105.0 |
| 2184 | Slight | 2019-12-26 | 97.0 |
| 2185 | Slight | 2019-12-27 | 157.0 |
| 2186 | Slight | 2019-12-28 | 140.0 |
| 2187 | Slight | 2019-12-29 | 118.0 |
| 2188 | Slight | 2019-12-30 | 191.0 |
| 2189 | Slight | 2019-12-31 | 168.0 |
def test_stationarity(df):
#Compute Rolling Stats:
rolling_mean = df.rolling(12).mean()
rolling_std = df.rolling(12).std()
#Plotting Rolling Stats:
rolling_mean['id'] = 'rolling-mean'
rolling_std['id'] = 'rolling-std'
df['id'] = 'original'
plotting = pd.concat([df, rolling_mean, rolling_std])
plotting.columns = ['value', 'id']
plotting['date'] = plotting.index
fig = px.line(plotting,
x='date',
y='value',
line_group='id',
hover_name='id',
color='id',
title='Viz for Rolling Stats')
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all")
])
)
)
fig.show()
#Dickey-Fuller Test:
print('ADFuller Test Results-', '\n')
df = df.drop(columns=['id'])
dftest = adfuller(df.iloc[:,0], autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,value in dftest[4].items():
dfoutput['Critical Value (%s)'%key] = value
print(dfoutput)
test_stationarity(time[time['Accident_Severity']=='Fatal'][['Date', 0]].set_index('Date'))
test_stationarity(time[time['Accident_Severity']=='Serious'][['Date', 0]].set_index('Date'))
test_stationarity(time[time['Accident_Severity']=='Slight'][['Date', 0]].set_index('Date'))
ADFuller Test Results- Test Statistic -9.186216e+00 p-value 2.184083e-15 #Lags Used 5.000000e+00 Number of Observations Used 7.240000e+02 Critical Value (1%) -3.439414e+00 Critical Value (5%) -2.865540e+00 Critical Value (10%) -2.568900e+00 dtype: float64
ADFuller Test Results- Test Statistic -4.012797 p-value 0.001345 #Lags Used 14.000000 Number of Observations Used 715.000000 Critical Value (1%) -3.439529 Critical Value (5%) -2.865591 Critical Value (10%) -2.568927 dtype: float64
ADFuller Test Results- Test Statistic -3.541004 p-value 0.006994 #Lags Used 20.000000 Number of Observations Used 709.000000 Critical Value (1%) -3.439607 Critical Value (5%) -2.865625 Critical Value (10%) -2.568945 dtype: float64
total = time[time['Accident_Severity'] == 'Slight'][['Date',
0]].set_index('Date')
total[0] = total[0] + time[time['Accident_Severity'] == 'Fatal'][[
'Date', 0
]].set_index('Date')[0] + time[time['Accident_Severity'] == 'Serious'][[
'Date', 0
]].set_index('Date')[0]
test_stationarity(total)
ADFuller Test Results- Test Statistic -3.558884 p-value 0.006598 #Lags Used 20.000000 Number of Observations Used 709.000000 Critical Value (1%) -3.439607 Critical Value (5%) -2.865625 Critical Value (10%) -2.568945 dtype: float64
total.reset_index(inplace=True)
total = total.resample('M', on='Date').sum()
decomposition = seasonal_decompose(total)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid
plt.subplot(411)
plt.plot(total, label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt.plot(trend, label='Trend')
plt.legend(loc='best')
plt.subplot(413)
plt.plot(seasonal,label='Seasonality')
plt.legend(loc='best')
plt.subplot(414)
plt.plot(residual, label='Residuals')
plt.legend(loc='best')
plt.tight_layout()
#px.line(seasonal, x=seasonal.index , y=0)
It can be noted from the rolling statistics graph for daily fatalities that there's a sudden increase in the number of deaths during New-Year's time as compared to other months. But this trend is quite the opposite for serious and slight accidents. This could be due to high number of drunk-driving cases on new-years. Looking at the total accident cases' rolling statistics show that the total accident cases drop the most during new-years but peak during the month of July 2018. There is also an indication of a rough trend of seasonality in the daily accident cases.
The Dickey-Fuller test of stationarity gives the result that the time series is stationary as the p-value < 0.05 and Test-statistic < Critical value at 5%.
But, this is the result of conducting the test on data that has a time frequency of 1 day. Due to this high frequency dataset, the noise increases and that results in an overall average of constant trend.
Thus, upon further investigation of monthly data, it is clear that there is a negative trend in the total number of accidents from 2018 to 2020 and a clear seasonal pattern can be detected. The cases rise sharply during the ending months of summer in July and August, then fall suddenly during September and shoot up to a maximum towards the end of the year in November and December. This repeated pattern (observed over the course of 2 years) has a direct correlation with the festive seasons.
day = P(acc, 'Accident_Severity', 'Day_of_Week')
day
| Accident_Severity | Day_of_Week | Fatal | Serious | Slight |
|---|---|---|---|---|
| 0 | Friday | 0.011830 | 0.185368 | 0.802802 |
| 1 | Monday | 0.013337 | 0.183867 | 0.802796 |
| 2 | Saturday | 0.016344 | 0.201794 | 0.781862 |
| 3 | Sunday | 0.017630 | 0.214514 | 0.767856 |
| 4 | Thursday | 0.011540 | 0.187545 | 0.800915 |
| 5 | Tuesday | 0.012207 | 0.183989 | 0.803804 |
| 6 | Wednesday | 0.011461 | 0.185758 | 0.802781 |
time1 = pd.DataFrame(acc.groupby('Time')['Accident_Severity'].value_counts()).unstack().unstack()
time1 = pd.DataFrame(time1['Accident_Severity']).reset_index()
time1.replace(np.nan, 0, inplace=True)
total1 = time1[time1['Accident_Severity'] == 'Slight'][['Time',
0]].set_index('Time')
total[0] = total[0] + time1[time1['Accident_Severity'] == 'Fatal'][[
'Time', 0
]].set_index('Time')[0] + time1[time1['Accident_Severity'] == 'Serious'][[
'Time', 0
]].set_index('Time')[0]
test_stationarity(total1)
ADFuller Test Results- Test Statistic -1.525997 p-value 0.520614 #Lags Used 24.000000 Number of Observations Used 1414.000000 Critical Value (1%) -3.434983 Critical Value (5%) -2.863586 Critical Value (10%) -2.567859 dtype: float64
Saturday and Sundays have a peak probability of Fatal accidents due to the fact that there are more vehicles on the road on the weekend since its a holiday. Looking at the time of the day for accidents, there are 2 sharp peaks in the accident rates, at around 8:30 AM and in the evening between 4-7 PM. This makes sense since these timings during the day are rush-hours and increase the probability of an accident.
Since the event of an accident is not only dependent on external environmental factors, but also on the other vehicles present on the road during the event, incorporating relevant traffic data would improve the predictive power of the dataset.
traffic = pd.read_csv('./data/dft_traffic_counts_raw_counts.csv')
traffic.head(50)
| count_point_id | direction_of_travel | year | count_date | hour | region_id | region_name | local_authority_id | local_authority_name | road_name | road_type | start_junction_road_name | end_junction_road_name | easting | northing | latitude | longitude | link_length_km | link_length_miles | pedal_cycles | two_wheeled_motor_vehicles | cars_and_taxis | buses_and_coaches | lgvs | hgvs_2_rigid_axle | hgvs_3_rigid_axle | hgvs_4_or_more_rigid_axle | hgvs_3_or_4_articulated_axle | hgvs_5_articulated_axle | hgvs_6_articulated_axle | all_hgvs | all_motor_vehicles | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 52 | E | 2019 | 06-09-2019 | 17 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 6 | 1 | 16 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
| 1 | 52 | E | 2019 | 06-09-2019 | 12 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 9 | 1 | 28 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 |
| 2 | 52 | E | 2019 | 06-09-2019 | 14 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 7 | 0 | 18 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 |
| 3 | 52 | W | 2019 | 06-09-2019 | 14 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 1 | 20 | 1 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 |
| 4 | 52 | W | 2019 | 06-09-2019 | 12 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 0 | 19 | 1 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 |
| 5 | 52 | W | 2019 | 06-09-2019 | 17 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 4 | 2 | 8 | 1 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 |
| 6 | 52 | W | 2019 | 06-09-2019 | 18 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 4 | 0 | 13 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
| 7 | 52 | E | 2019 | 06-09-2019 | 9 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 4 | 0 | 19 | 0 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 32 |
| 8 | 52 | W | 2019 | 06-09-2019 | 10 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 1 | 24 | 1 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 |
| 9 | 52 | E | 2019 | 06-09-2019 | 8 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 4 | 1 | 9 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 |
| 10 | 52 | E | 2019 | 06-09-2019 | 18 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 0 | 1 | 10 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
| 11 | 52 | W | 2019 | 06-09-2019 | 15 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 5 | 1 | 27 | 1 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 51 |
| 12 | 52 | E | 2019 | 06-09-2019 | 13 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 8 | 0 | 22 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 13 | 52 | E | 2019 | 06-09-2019 | 10 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 7 | 1 | 20 | 0 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 30 |
| 14 | 52 | W | 2019 | 06-09-2019 | 16 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 6 | 2 | 17 | 1 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
| 15 | 52 | E | 2019 | 06-09-2019 | 11 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 8 | 2 | 14 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 |
| 16 | 52 | E | 2019 | 06-09-2019 | 7 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 1 | 4 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 17 | 52 | W | 2019 | 06-09-2019 | 7 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 4 | 0 | 10 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
| 18 | 52 | W | 2019 | 06-09-2019 | 8 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 41 | 2 | 33 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 |
| 19 | 52 | W | 2019 | 06-09-2019 | 13 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 0 | 15 | 0 | 11 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 27 |
| 20 | 52 | W | 2019 | 06-09-2019 | 9 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 10 | 3 | 20 | 1 | 16 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 41 |
| 21 | 52 | E | 2019 | 06-09-2019 | 15 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 29 | 1 | 33 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 |
| 22 | 52 | W | 2019 | 06-09-2019 | 11 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 3 | 2 | 7 | 1 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 |
| 23 | 52 | E | 2019 | 06-09-2019 | 16 | 1 | South West | 1 | Isles of Scilly | A3112 | Major | A3111 | A3110 | 91203 | 10217 | 49.912239 | -6.302848 | 2.0 | 1.24 | 9 | 1 | 13 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 |
| 24 | 6016 | W | 2019 | 02-10-2019 | 14 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 5 | 1522 | 7 | 554 | 44 | 19 | 11 | 18 | 67 | 95 | 254 | 2342 |
| 25 | 6016 | E | 2019 | 02-10-2019 | 10 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 5 | 1124 | 14 | 351 | 52 | 12 | 15 | 20 | 90 | 84 | 273 | 1767 |
| 26 | 6016 | W | 2019 | 02-10-2019 | 9 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 12 | 1352 | 11 | 336 | 19 | 10 | 11 | 10 | 85 | 43 | 178 | 1889 |
| 27 | 6016 | E | 2019 | 02-10-2019 | 7 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 9 | 2284 | 11 | 701 | 55 | 13 | 20 | 9 | 72 | 58 | 227 | 3232 |
| 28 | 6016 | E | 2019 | 02-10-2019 | 9 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 5 | 1434 | 14 | 411 | 47 | 10 | 10 | 11 | 86 | 84 | 248 | 2112 |
| 29 | 6016 | E | 2019 | 02-10-2019 | 13 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 4 | 1337 | 10 | 366 | 48 | 7 | 14 | 14 | 102 | 77 | 262 | 1979 |
| 30 | 6016 | W | 2019 | 02-10-2019 | 15 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 5 | 1843 | 11 | 614 | 61 | 8 | 7 | 13 | 67 | 112 | 268 | 2741 |
| 31 | 6016 | W | 2019 | 02-10-2019 | 16 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 7 | 1468 | 8 | 446 | 22 | 4 | 14 | 6 | 29 | 52 | 127 | 2056 |
| 32 | 6016 | E | 2019 | 02-10-2019 | 16 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 6 | 1999 | 7 | 407 | 42 | 1 | 3 | 13 | 78 | 62 | 199 | 2618 |
| 33 | 6016 | E | 2019 | 02-10-2019 | 11 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 1 | 1248 | 10 | 333 | 36 | 7 | 9 | 15 | 82 | 94 | 243 | 1835 |
| 34 | 6016 | W | 2019 | 02-10-2019 | 10 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 1 | 1307 | 18 | 341 | 31 | 12 | 14 | 14 | 96 | 78 | 245 | 1912 |
| 35 | 6016 | E | 2019 | 02-10-2019 | 17 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 8 | 1981 | 14 | 286 | 23 | 2 | 2 | 12 | 85 | 74 | 198 | 2487 |
| 36 | 6016 | E | 2019 | 02-10-2019 | 18 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 4 | 1422 | 10 | 224 | 26 | 2 | 1 | 9 | 77 | 46 | 161 | 1821 |
| 37 | 6016 | W | 2019 | 02-10-2019 | 8 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 11 | 1657 | 1 | 349 | 29 | 5 | 9 | 6 | 78 | 80 | 207 | 2225 |
| 38 | 6016 | E | 2019 | 02-10-2019 | 8 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 8 | 2174 | 8 | 574 | 87 | 11 | 18 | 6 | 63 | 105 | 290 | 3054 |
| 39 | 6016 | E | 2019 | 02-10-2019 | 14 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 4 | 1403 | 20 | 407 | 50 | 6 | 13 | 9 | 128 | 74 | 280 | 2114 |
| 40 | 6016 | E | 2019 | 02-10-2019 | 15 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 9 | 1728 | 11 | 429 | 36 | 6 | 9 | 19 | 92 | 91 | 253 | 2430 |
| 41 | 6016 | W | 2019 | 02-10-2019 | 7 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 7 | 1961 | 1 | 481 | 40 | 9 | 5 | 6 | 79 | 96 | 235 | 2685 |
| 42 | 6016 | E | 2019 | 02-10-2019 | 12 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 1 | 1157 | 12 | 334 | 45 | 7 | 11 | 13 | 93 | 79 | 248 | 1752 |
| 43 | 6016 | W | 2019 | 02-10-2019 | 11 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 8 | 1254 | 3 | 378 | 27 | 14 | 8 | 13 | 74 | 76 | 212 | 1855 |
| 44 | 6016 | W | 2019 | 02-10-2019 | 12 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 7 | 1243 | 15 | 387 | 46 | 7 | 17 | 14 | 79 | 97 | 260 | 1912 |
| 45 | 6016 | W | 2019 | 02-10-2019 | 13 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 7 | 1247 | 9 | 448 | 46 | 8 | 20 | 15 | 45 | 88 | 222 | 1933 |
| 46 | 6016 | W | 2019 | 02-10-2019 | 17 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 12 | 1696 | 5 | 351 | 15 | 3 | 2 | 4 | 18 | 44 | 86 | 2150 |
| 47 | 6016 | W | 2019 | 02-10-2019 | 18 | 1 | South West | 68 | Wiltshire | M4 | Major | LA Boundary | 17 | 389200 | 179080 | 51.510542 | -2.157015 | 10.0 | 6.21 | 0 | 7 | 1613 | 7 | 249 | 14 | 3 | 3 | 8 | 22 | 23 | 73 | 1949 |
| 48 | 6019 | S | 2019 | 17-06-2019 | 10 | 1 | South West | 70 | Gloucestershire | M5 | Major | 11 | 10 | 389660 | 223200 | 51.907239 | -2.151710 | 4.7 | 2.92 | 0 | 2 | 2651 | 15 | 519 | 133 | 14 | 13 | 17 | 35 | 250 | 462 | 3649 |
| 49 | 6019 | S | 2019 | 17-06-2019 | 12 | 1 | South West | 70 | Gloucestershire | M5 | Major | 11 | 10 | 389660 | 223200 | 51.907239 | -2.151710 | 4.7 | 2.92 | 0 | 14 | 2548 | 16 | 423 | 107 | 14 | 17 | 23 | 21 | 198 | 380 | 3381 |
traffic.year.value_counts()
2019 301944 2018 289848 2017 186468 2016 166824 2015 103491 Name: year, dtype: int64
Since the study only involves 2018 and 2019, drop the previous years
traffic = traffic[(traffic['year']==2018) | (traffic['year']==2019)]
traffic.shape
(591792, 32)
Computing the mean number of vehicle_types for each count_point_id and hour of the day
traffic['all_gvs'] = traffic['all_hgvs']+traffic['lgvs']+traffic1['buses_and_coaches']
traffic1 = pd.DataFrame(
traffic.groupby(['count_point_id',
'hour', 'latitude', 'longitude', 'year'])['link_length_km', 'pedal_cycles',
'two_wheeled_motor_vehicles', 'cars_and_taxis',
'all_gvs'].sum()).reset_index()
traffic1 = pd.DataFrame(
traffic1.groupby(['count_point_id',
'hour', 'latitude', 'longitude'])['link_length_km', 'pedal_cycles',
'two_wheeled_motor_vehicles', 'cars_and_taxis',
'all_gvs'].mean()).reset_index()
traffic1
| count_point_id | hour | latitude | longitude | link_length_km | pedal_cycles | two_wheeled_motor_vehicles | cars_and_taxis | all_gvs | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 52 | 7 | 49.912239 | -6.302848 | 4.0 | 7.0 | 1.0 | 14.0 | 19.0 |
| 1 | 52 | 8 | 49.912239 | -6.302848 | 4.0 | 45.0 | 3.0 | 42.0 | 27.0 |
| 2 | 52 | 9 | 49.912239 | -6.302848 | 4.0 | 14.0 | 3.0 | 39.0 | 36.0 |
| 3 | 52 | 10 | 49.912239 | -6.302848 | 4.0 | 10.0 | 2.0 | 44.0 | 31.0 |
| 4 | 52 | 11 | 49.912239 | -6.302848 | 4.0 | 11.0 | 4.0 | 21.0 | 39.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 251347 | 996768 | 14 | 57.146594 | -2.088728 | 0.0 | 0.0 | 0.0 | 394.0 | 0.0 |
| 251348 | 996768 | 15 | 57.146594 | -2.088728 | 0.0 | 0.0 | 0.0 | 213.0 | 0.0 |
| 251349 | 996768 | 16 | 57.146594 | -2.088728 | 0.0 | 0.0 | 0.0 | 416.0 | 0.0 |
| 251350 | 996768 | 17 | 57.146594 | -2.088728 | 0.0 | 0.0 | 0.0 | 332.0 | 0.0 |
| 251351 | 996768 | 18 | 57.146594 | -2.088728 | 0.0 | 0.0 | 0.0 | 150.0 | 0.0 |
251352 rows × 9 columns
traffic1.shape
(251352, 9)
del traffic
traffic1.hour.value_counts()
18 20946 17 20946 16 20946 15 20946 14 20946 13 20946 12 20946 11 20946 10 20946 9 20946 8 20946 7 20946 Name: hour, dtype: int64
def plot(col):
tplot = pd.DataFrame(traffic1.groupby('hour')[col].mean()).reset_index()
fig = px.line(tplot, x='hour', y=col)
fig.show()
plot('cars_and_taxis')
tplot = pd.DataFrame(traffic1.groupby('hour')['cars_and_taxis'].mean()).reset_index()
tplot['hr_sin'] = tplot.hour*(2.*np.pi/24)
x = tplot['hr_sin']
y = tplot['cars_and_taxis']
f = interpolate.interp1d(x, y, kind='nearest', fill_value = 'extrapolate')
xnew = np.append(np.array(tplot['hour']), [0,1,2,3,4,5,6,19,20,21,22,23])
xnew = xnew*(2.*np.pi/24)
ynew = f(xnew)
plt.plot(x, y, 'o', xnew, ynew, '-')
plt.show()
Since every count_point_id has vehicle counts for only the major hours of the day when the traffic is at its peak (0007 - 1800), the method of cubic spline interpolation is tried in order to compute the missing values for hours between 0000-0007 and 1800-0000. Since this must be done for each and every count_point_id individually since each of them have varying traffic counts, there are extremely less datums per counting point for efficient extrapolation of the function on the uncovered hours. Thus, instead of using raw traffic data per hour, AADF traffic data is used for the years 2018-2019
del traffic1
The annual average daily flow (AADF) of traffic is measured at various points on major and minor roads across the UK, according to the type of vehicle.
traffic = pd.read_csv('./data/dft_traffic_counts_aadf.csv')
traffic = traffic[(traffic['year']==2018) | (traffic['year']==2019)]
traffic = traffic[[
'count_point_id', 'road_name', 'latitude', 'longitude', 'pedal_cycles',
'two_wheeled_motor_vehicles', 'cars_and_taxis', 'buses_and_coaches', 'lgvs', 'all_hgvs'
]]
traffic = pd.DataFrame(traffic.groupby('count_point_id').mean()).reset_index()
traffic.head()
| count_point_id | latitude | longitude | pedal_cycles | two_wheeled_motor_vehicles | cars_and_taxis | buses_and_coaches | lgvs | all_hgvs | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 51 | 49.915021 | -6.317073 | 244.0 | 96.5 | 545.5 | 25.0 | 450.0 | 43.5 |
| 1 | 52 | 49.912291 | -6.304267 | 163.0 | 51.0 | 537.5 | 7.5 | 247.5 | 8.5 |
| 2 | 53 | 49.917139 | -6.306114 | 184.5 | 88.5 | 795.0 | 7.0 | 427.5 | 21.5 |
| 3 | 54 | 49.917809 | -6.298996 | 63.0 | 55.0 | 346.5 | 6.0 | 298.5 | 37.0 |
| 4 | 55 | 49.918583 | -6.295093 | 34.5 | 20.5 | 167.5 | 0.0 | 179.5 | 16.0 |
The population density of an area (LSOA) indicates how crowded a region is and thus adds on to the data that indicates the intensity of activity in each region. This helps understand the factors that contribute to the event of an accident and may thus improve the predictive power of the Classifier.
The population counts of all areas of UK have been used from UK's most recent national Census of 2011.
population = pd.read_csv('./data/census_2011.csv')
population.head()
| date | geography | geography code | Rural Urban | Variable: All usual residents; measures: Value | Variable: Males; measures: Value | Variable: Females; measures: Value | Variable: Lives in a household; measures: Value | Variable: Lives in a communal establishment; measures: Value | Variable: Schoolchild or full-time student aged 4 and over at their non term-time address; measures: Value | Variable: Area (Hectares); measures: Value | Variable: Density (number of persons per hectare); measures: Value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | Darlington 001B | E01012334 | Total | 2466 | 1220 | 1246 | 2466 | 0 | 42 | 6033.58 | 0.4 |
| 1 | 2011 | Darlington 001C | E01012335 | Total | 1383 | 682 | 701 | 1383 | 0 | 21 | 114.01 | 12.1 |
| 2 | 2011 | Darlington 001D | E01012366 | Total | 2008 | 972 | 1036 | 2008 | 0 | 30 | 6718.16 | 0.3 |
| 3 | 2011 | Darlington 001E | E01033481 | Total | 1364 | 680 | 684 | 1350 | 14 | 21 | 146.95 | 9.3 |
| 4 | 2011 | Darlington 001F | E01033482 | Total | 1621 | 810 | 811 | 1621 | 0 | 16 | 234.31 | 6.9 |
population = population[['geography code', 'Variable: Density (number of persons per hectare); measures: Value']]
population.columns = ['LSOA', 'pop_density']
population.head()
| LSOA | pop_density | |
|---|---|---|
| 0 | E01012334 | 0.4 |
| 1 | E01012335 | 12.1 |
| 2 | E01012366 | 0.3 |
| 3 | E01033481 | 9.3 |
| 4 | E01033482 | 6.9 |
Merging Population Density data with accident data using LSOA
data = pd.merge(left=acc, right=population, how='left', left_on='LSOA_of_Accident_Location', right_on='LSOA')
data
| Accident_Index | LSOA_of_Accident_Location | Longitude | Latitude | Date | Day_of_Week | Time | 1st_Road_Class | Road_Type | Speed_limit | Junction_Detail | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Urban_or_Rural_Area | Accident_Severity | LSOA | pop_density | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | E01000854 | -0.139737 | 51.524587 | 01/01/2018 | Monday | 01:30 | A | Dual carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01000854 | 97.1 |
| 1 | 2018010080973 | E01003531 | 0.046471 | 51.539651 | 01/01/2018 | Monday | 00:50 | B | Single carriageway | 30 MPH | Mini-roundabout | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01003531 | 89.8 |
| 2 | 2018010080974 | E01002723 | -0.102474 | 51.529746 | 01/01/2018 | Monday | 00:45 | A | Single carriageway | 20 MPH | Crossroads | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01002723 | 149.2 |
| 3 | 2018010080981 | E01003492 | 0.037828 | 51.530179 | 01/01/2018 | Monday | 03:00 | B | Dual carriageway | 30 MPH | More than 4 arms (not roundabout) | Darkness - lights lit | Raining no high winds | Wet or damp | Urban | Serious | E01003492 | 212.0 |
| 4 | 2018010080982 | E01001682 | 0.065781 | 51.469258 | 01/01/2018 | Monday | 02:20 | A | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Wet or damp | Urban | Serious | E01001682 | 15.3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 227936 | 201963DF02919 | W01000457 | -3.094841 | 52.739912 | 17/08/2019 | Saturday | 16:29 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000457 | 0.5 |
| 227937 | 201963DF03019 | W01000427 | -3.322442 | 52.335266 | 25/08/2019 | Sunday | 11:40 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000427 | 0.1 |
| 227938 | 201963DF03319 | W01000429 | -3.515306 | 52.506924 | 22/09/2019 | Sunday | 23:15 | B | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Raining no high winds | Wet or damp | Rural | Fatal | W01000429 | 0.1 |
| 227939 | 201963DF03419 | W01000471 | -3.651922 | 52.097790 | 05/10/2019 | Saturday | 12:50 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000471 | 0.1 |
| 227940 | 201963DF03619 | W01000474 | -3.544971 | 51.921926 | 03/11/2019 | Sunday | 15:10 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000474 | 0.1 |
227941 rows × 18 columns
data.shape
(227941, 18)
In order to merge traffic value counts with the accident data, there is no common key value such as LSOA. The keys for traffic counts are the count_point_ids that indicate the roads where the counts were taken. Thus, in order to add traffic data to the accident events, the (latitude, longitude) coordinates are used. By comparing the nearest/closest coordinates of traffic count_point_ids and the accident events, the traffic counts can be added to each accident event.
A simple brute force search for the nearest neighbors amongst the two datasets would take an immense amount of time due to the large volume of data. Thus, the KDTree algorithm (using the sliding midpoint rule), as described in Maneewongvatana and Mount 1999, is used in order to efficiently calculate the nearest neighbor for each coordinate across datasets.
tree = cKDTree(traffic[['longitude', 'latitude']])
d,i = tree.query(data[['Longitude', 'Latitude']])
traffic = traffic.loc[i]
traffic.reset_index(inplace=True, drop=True)
data = pd.concat([data, traffic], axis=1)
data
| Accident_Index | LSOA_of_Accident_Location | Longitude | Latitude | Date | Day_of_Week | Time | 1st_Road_Class | Road_Type | Speed_limit | Junction_Detail | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Urban_or_Rural_Area | Accident_Severity | LSOA | pop_density | count_point_id | latitude | longitude | pedal_cycles | two_wheeled_motor_vehicles | cars_and_taxis | buses_and_coaches | lgvs | all_hgvs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | E01000854 | -0.139737 | 51.524587 | 01/01/2018 | Monday | 01:30 | A | Dual carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01000854 | 97.1 | 47245 | 51.524223 | -0.141882 | 1552.5 | 3982.0 | 53957.5 | 1822.5 | 14522.0 | 1678.0 |
| 1 | 2018010080973 | E01003531 | 0.046471 | 51.539651 | 01/01/2018 | Monday | 00:50 | B | Single carriageway | 30 MPH | Mini-roundabout | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01003531 | 89.8 | 942818 | 51.536955 | 0.040555 | 19.5 | 5.0 | 277.5 | 2.0 | 46.0 | 1.0 |
| 2 | 2018010080974 | E01002723 | -0.102474 | 51.529746 | 01/01/2018 | Monday | 00:45 | A | Single carriageway | 20 MPH | Crossroads | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | E01002723 | 149.2 | 57902 | 51.530545 | -0.102034 | 346.0 | 1302.0 | 10112.5 | 358.0 | 3115.0 | 603.5 |
| 3 | 2018010080981 | E01003492 | 0.037828 | 51.530179 | 01/01/2018 | Monday | 03:00 | B | Dual carriageway | 30 MPH | More than 4 arms (not roundabout) | Darkness - lights lit | Raining no high winds | Wet or damp | Urban | Serious | E01003492 | 212.0 | 942818 | 51.536955 | 0.040555 | 19.5 | 5.0 | 277.5 | 2.0 | 46.0 | 1.0 |
| 4 | 2018010080982 | E01001682 | 0.065781 | 51.469258 | 01/01/2018 | Monday | 02:20 | A | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Wet or damp | Urban | Serious | E01001682 | 15.3 | 73053 | 51.468839 | 0.069104 | 106.5 | 294.5 | 14014.0 | 466.5 | 2020.5 | 119.5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 227936 | 201963DF02919 | W01000457 | -3.094841 | 52.739912 | 17/08/2019 | Saturday | 16:29 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000457 | 0.5 | 812002 | 52.759171 | -3.084242 | 23.0 | 3.0 | 810.0 | 13.0 | 181.0 | 48.0 |
| 227937 | 201963DF03019 | W01000427 | -3.322442 | 52.335266 | 25/08/2019 | Sunday | 11:40 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000427 | 0.1 | 50547 | 52.300400 | -3.321252 | 0.0 | 50.5 | 2765.5 | 21.0 | 684.5 | 141.5 |
| 227938 | 201963DF03319 | W01000429 | -3.515306 | 52.506924 | 22/09/2019 | Sunday | 23:15 | B | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Raining no high winds | Wet or damp | Rural | Fatal | W01000429 | 0.1 | 951275 | 52.481222 | -3.527229 | 3.0 | 5.0 | 941.5 | 11.5 | 175.0 | 18.5 |
| 227939 | 201963DF03419 | W01000471 | -3.651922 | 52.097790 | 05/10/2019 | Saturday | 12:50 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000471 | 0.1 | 20554 | 52.133923 | -3.608538 | 0.0 | 28.0 | 1642.0 | 18.5 | 486.5 | 239.5 |
| 227940 | 201963DF03619 | W01000474 | -3.544971 | 51.921926 | 03/11/2019 | Sunday | 15:10 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | W01000474 | 0.1 | 50653 | 51.912263 | -3.529769 | 3.5 | 64.5 | 791.0 | 0.0 | 337.5 | 45.0 |
227941 rows × 27 columns
data.shape
(227941, 27)
Now that the datasets have been merged, looking at the distances between coordinates from each dataset that have been picked as nearest neighbors
# Haversine Formula for computing distances between coordinates
def distance(lat1, lon1, lat2, lon2):
p = 0.017453292519943295
a = 0.5 - cos(
(lat2 - lat1) * p) / 2 + cos(lat1 * p) * cos(lat2 * p) * (1 - cos(
(lon2 - lon1) * p)) / 2
return (12742 * asin(sqrt(a))) * 1000
data['distance'] = data.apply(lambda x: distance(x.latitude, x.longitude, x.Latitude, x.Longitude), axis=1)
data['distance'].describe()
count 227941.000000 mean 590.496963 std 673.412777 min 0.000000 25% 194.007850 50% 385.870443 75% 711.834689 max 16284.173724 Name: distance, dtype: float64
The statistics of the distances between the 2 set of coordinates show that a majority of the distances are under 711 meters. Thus, the count points have more or less been successfully merged according to the coordinates. The max distance is 16kms and that may be due to the unavailability of a counting point near the accident location due to it being a non-major road in a rural area.
data.columns
Index(['Accident_Index', 'LSOA_of_Accident_Location', 'Longitude', 'Latitude',
'Date', 'Day_of_Week', 'Time', '1st_Road_Class', 'Road_Type',
'Speed_limit', 'Junction_Detail', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions', 'Urban_or_Rural_Area',
'Accident_Severity', 'LSOA', 'pop_density', 'count_point_id',
'latitude', 'longitude', 'pedal_cycles', 'two_wheeled_motor_vehicles',
'cars_and_taxis', 'buses_and_coaches', 'lgvs', 'all_hgvs', 'distance'],
dtype='object')
data = data[['Accident_Index', 'Longitude', 'Latitude',
'Date', 'Day_of_Week', 'Time', '1st_Road_Class', 'Road_Type',
'Speed_limit', 'Junction_Detail', 'Light_Conditions',
'Weather_Conditions', 'Road_Surface_Conditions', 'Urban_or_Rural_Area',
'Accident_Severity', 'pop_density', 'count_point_id',
'pedal_cycles', 'two_wheeled_motor_vehicles',
'cars_and_taxis', 'buses_and_coaches', 'lgvs', 'all_hgvs']]
Time stamp based categorical features have been converted to cyclic - sinusoidal based continuous valued variables. This is to emphasize on the cyclic nature of time during the day, days of the week and the months of the year. This helps establish numeric relations within the feature and add more context to the judgement of the predictor.
repl = pd.read_excel(f'./data/keys/variable lookup_Day of Week.xls')
data = data.replace({
'Day_of_Week':
dict([(i, a) for i, a in zip(repl['label'], repl['code'])])
})
def date_to_sin(s):
d = {0:0, 1:31, 2:30, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30, 10:31, 11:30, 12:31}
day = int(s[:2]) + np.sum([d[i] for i in range(int(s[3:5]))])
return sin(day*2*np.pi/365)
data['Time'] = data['Time'].apply(lambda x: sin((3600*int(x[:2]) + 60*int(x[3:]))*2*np.pi/(24*3600)))
data['Day_of_Week'] = data['Day_of_Week'].apply(lambda x: sin(x*2*np.pi/7))
data['Date'] = data['Date'].apply(lambda x: date_to_sin(x))
data
| Accident_Index | Longitude | Latitude | Date | Day_of_Week | Time | 1st_Road_Class | Road_Type | Speed_limit | Junction_Detail | Light_Conditions | Weather_Conditions | Road_Surface_Conditions | Urban_or_Rural_Area | Accident_Severity | pop_density | count_point_id | pedal_cycles | two_wheeled_motor_vehicles | cars_and_taxis | buses_and_coaches | lgvs | all_hgvs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018010080971 | -0.139737 | 51.524587 | 0.017213 | 9.749279e-01 | 0.382683 | A | Dual carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | 97.1 | 47245 | 1552.5 | 3982.0 | 53957.5 | 1822.5 | 14522.0 | 1678.0 |
| 1 | 2018010080973 | 0.046471 | 51.539651 | 0.017213 | 9.749279e-01 | 0.216440 | B | Single carriageway | 30 MPH | Mini-roundabout | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | 89.8 | 942818 | 19.5 | 5.0 | 277.5 | 2.0 | 46.0 | 1.0 |
| 2 | 2018010080974 | -0.102474 | 51.529746 | 0.017213 | 9.749279e-01 | 0.195090 | A | Single carriageway | 20 MPH | Crossroads | Darkness - lights lit | Fine no high winds | Dry | Urban | Slight | 149.2 | 57902 | 346.0 | 1302.0 | 10112.5 | 358.0 | 3115.0 | 603.5 |
| 3 | 2018010080981 | 0.037828 | 51.530179 | 0.017213 | 9.749279e-01 | 0.707107 | B | Dual carriageway | 30 MPH | More than 4 arms (not roundabout) | Darkness - lights lit | Raining no high winds | Wet or damp | Urban | Serious | 212.0 | 942818 | 19.5 | 5.0 | 277.5 | 2.0 | 46.0 | 1.0 |
| 4 | 2018010080982 | 0.065781 | 51.469258 | 0.017213 | 9.749279e-01 | 0.573576 | A | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Fine no high winds | Wet or damp | Urban | Serious | 15.3 | 73053 | 106.5 | 294.5 | 14014.0 | 466.5 | 2020.5 | 119.5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 227936 | 201963DF02919 | -3.094841 | 52.739912 | -0.741222 | -2.449294e-16 | -0.922201 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | 0.5 | 812002 | 23.0 | 3.0 | 810.0 | 13.0 | 181.0 | 48.0 |
| 227937 | 201963DF03019 | -3.322442 | 52.335266 | -0.826354 | 7.818315e-01 | 0.087156 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | 0.1 | 50547 | 0.0 | 50.5 | 2765.5 | 21.0 | 684.5 | 141.5 |
| 227938 | 201963DF03319 | -3.515306 | 52.506924 | -0.993257 | 7.818315e-01 | -0.195090 | B | Single carriageway | 30 MPH | Not at junction or within 20 metres | Darkness - lights lit | Raining no high winds | Wet or damp | Rural | Fatal | 0.1 | 951275 | 3.0 | 5.0 | 941.5 | 11.5 | 175.0 | 18.5 |
| 227939 | 201963DF03419 | -3.651922 | 52.097790 | -0.994218 | -2.449294e-16 | -0.216440 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | 0.1 | 20554 | 0.0 | 28.0 | 1642.0 | 18.5 | 486.5 | 239.5 |
| 227940 | 201963DF03619 | -3.544971 | 51.921926 | -0.821477 | 7.818315e-01 | -0.737277 | A | Single carriageway | 60 MPH | Not at junction or within 20 metres | Daylight | Fine no high winds | Dry | Rural | Fatal | 0.1 | 50653 | 3.5 | 64.5 | 791.0 | 0.0 | 337.5 | 45.0 |
227941 rows × 23 columns
data.to_pickle('./data/struct_dataset.pkl')